diff --git a/docs/source/_static/JOINT_DAG.png b/docs/source/_static/JOINT_DAG.png new file mode 100644 index 00000000..4ce8f04e Binary files /dev/null and b/docs/source/_static/JOINT_DAG.png differ diff --git a/docs/source/_static/forwards_backwards.png b/docs/source/_static/forwards_backwards.png new file mode 100644 index 00000000..dbaaf71e Binary files /dev/null and b/docs/source/_static/forwards_backwards.png differ diff --git a/docs/source/_static/potential_outcomes.png b/docs/source/_static/potential_outcomes.png new file mode 100644 index 00000000..f8c9e2f7 Binary files /dev/null and b/docs/source/_static/potential_outcomes.png differ diff --git a/docs/source/_static/probabilistic_intervention_fix.png b/docs/source/_static/probabilistic_intervention_fix.png new file mode 100644 index 00000000..550b23ac Binary files /dev/null and b/docs/source/_static/probabilistic_intervention_fix.png differ diff --git a/docs/source/knowledgebase/glossary.rst b/docs/source/knowledgebase/glossary.rst index 1f33df2e..4d971590 100644 --- a/docs/source/knowledgebase/glossary.rst +++ b/docs/source/knowledgebase/glossary.rst @@ -66,6 +66,9 @@ Glossary Pretest-posttest design A quasi-experimental design where the treatment effect is estimated by comparing an outcome measure before and after treatment. + Probabilistic Programming + Probabilistic programming is the practice of expressing statistical using general-purpose programming languages extended with constructs for random variables, probability distributions, and inference. Prominent examples are `PyMC` and `Stan` + Propensity scores An estimate of the probability of adopting a treatment status. Used in re-weighting schemes to balance observational data. diff --git a/docs/source/knowledgebase/index.md b/docs/source/knowledgebase/index.md index 94a573c0..1a253cf1 100644 --- a/docs/source/knowledgebase/index.md +++ b/docs/source/knowledgebase/index.md @@ -6,6 +6,7 @@ glossary design_notation quasi_dags.ipynb +structural_causal_models.ipynb causal_video_resources causal_written_resources ::: diff --git a/docs/source/knowledgebase/structural_causal_models.ipynb b/docs/source/knowledgebase/structural_causal_models.ipynb new file mode 100644 index 00000000..d8eeb9b1 --- /dev/null +++ b/docs/source/knowledgebase/structural_causal_models.ipynb @@ -0,0 +1,5604 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "import warnings\n", + "\n", + "import arviz as az\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "import pymc_bart as pmb\n", + "import pytensor.tensor as pt\n", + "import statsmodels.formula.api as smf\n", + "from matplotlib import pyplot as plt\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian Structural Causal Inference\n", + "\n", + "When we ask \"What is the effect of a medical treatment?\" or \"Does quitting smoking cause weight gain?\" or \"Do job training programs increase earnings?\", we are not simply asking about the treatment itself. We are asking: What world are we operating in? This perspective is more easily seen if you imagine a causal analyst as a pet-shop owner introducing a new fish to one of their many acquariums. The new fish's survival and behavior depend less on its intrinsic properties than on how it fits within this complex, interconnected system of PH balances and predators. In which tank will the new fish thrive? \n", + "\n", + "Different causal methods make different choices about how much of this system to model explicitly. Some methods succeed by not modeling the full system: instrumental variables isolate causal effects through credible exclusion restrictions; difference-in-differences leverages parallel trends; interrupted time-series assumes stationarity. These design-based approaches gain power by minimizing modeling assumptions about the data-generating process. See {cite:t}`pearl2000causality` or {cite:t}`angrist2009mostly` for more detailed distinctions. The unifying thread between these diverse methods is the idea of a causal model as a _probabilistic program_ : an inferential routine designed to explicitly yield insights into the effect of some intervention or treatment on the system of interest. Whether design based or model-based, causal inference methods assume a data generating process - the distinction between these methods is how explicitly the system is rendered.\n", + "\n", + "#### Modelling Worlds and Counterfactual Worlds\n", + "\n", + "Bayesian structural modeling attempts to parameterize the system itself. Where design-based methods answer \"what is the causal effect under these identification assumptions?\", structural models ask \"what is the most plausible data-generating process, and how do interventions propagate through it?\". In Bayesian structural causal inference the focus is slightly different in that we wish to model both the treatment and the environment i.e. the fish and the fishtank. The trade-off is transparency for complexity. You must specify more of the data-generating process, which creates more opportunities for model misspecification. But every assumption becomes an explicit, testable model component rather than an implicit background condition.\n", + "\n", + "This is a two step move in the Bayesian paradigm. First we infer \"backwards\" what is the most plausible state of the world $w$ conditioned on the observable data. The \"world\" of the model is defined by: (1) a causal graph relating variables, (2) likelihood functions specifying how each variable depends on its causes, and (3) prior distributions over parameters. Optionally, this may include latent confounders, measurement models, and selection mechanisms—each adding structural detail but also complexity. With this world in place, we continue to assess the probabilistic predictive distribution of treatment and outcome at the plausible range of counterfactual worlds. \n", + "\n", + "![](../_static/forwards_backwards.png)\n", + "\n", + "The important point is that we characterise the plausible worlds by how much structure we learn about in the model specification. The more structure we seek to infer, the more we risk model misspecification, but simultaneously, the more structure we learn the more useful and transparent our conclusions. This structural commitment contrasts sharply with reduced-form approaches that minimize explicit modeling.\n", + "\n", + "#### Minimalism and Structural Maximalism\n", + "\n", + "The term \"reduced form\" originates from econometric simultaneous equations models. Early economists wanted to model supply and demand as functions of price, but faced a problem: quantities also determine price in competitive markets. Because these structural relationships are mutually determined, the system is hard to solve directly. The solution was algebraic transformation: solve for the 'reduced form' that expresses endogenous variables purely as functions of exogenous ones.\n", + "\n", + "Reduced form systems are transformed systems of interest designed to estimate the focal parameters by leveraging observable and tractable data. These approaches eschew \"theory driven\" model specifications in favour of models with precise _identifiable estimands_. This approach - transforming complex structural systems into tractable estimating equations - reflects a broader methodological commitment. It is for this minimalist preference that they are typically contrasted with structural models that aim to express the \"fuller\" data generating process. Design based causal inference methods typically adopt this focus on identifiability within a regression framework. For richer discussion in this vein see {cite:t}`hansenEconometrics` or {cite:t}`aronowFoundations`. \n", + "\n", + "When we regress an outcome $Y$ on a treatment $T$ and a set of covariates $X$,\n", + "\n", + "$$Y = \\alpha T + X \\beta + \\epsilon$$\n", + "\n", + "the coefficient $\\alpha$ captures the average change in Y associated with a one-unit change in $T$. Only under strong assumptions, however, can we interpret this as a causal effect. In real-world settings, those assumptions (like exogeneity of $T$) are fragile:\n", + "\n", + "- Confounding: Unobserved or omitted variables affect both \n", + "$T$ and $Y$.\n", + "\n", + "- Endogeneity: Treatment assignment mechanisms are correlated with the error term.\n", + "\n", + "- Measurement uncertainty: Model parameters and predictions have uncertainty not captured by point estimates.\n", + "\n", + "The innovative methods of inference (like Two-stage least squares, propensity score weighting or DiD designs) that came to define the _credibility revolution_ in the social sciences, seek to overcome this risk of confounding with constraints or assumptions to bolster identification of the causal parameters. See See {cite:t}`angrist2009mostly`. Bayesian probabilistic causal inference addresses these challenges by explicitly modelling the data-generating process and quantifying all sources of uncertainty. Rather than point estimates and design assumptions, we infer full posterior distributions over causal parameters and even over counterfactual outcomes. Rather than isolating the outcome equation from the treatment equation, we model them together as parts of a single generative system. This approach mirrors how interventions occur in the real world. The propensity for adopting a treatment can be predicted by the same factors which determine treatment outcomes. This structure creates the risk of confounding because the efficacy of the treatment is obscured by the influence of these shared predictors. When we fit such a model, we learn about every component simultaneously—the effect of the treatment, the influence of confounders, and the uncertainty that ties them together. Once fitted, Bayesian models can generate posterior predictive draws for “what if” scenarios. This capacity lets us compute causal estimands like the ATE or individual treatment effects directly from the posterior.\n", + "\n", + "In this tutorial, we’ll move step by step from data simulation to Structural Bayesian Causal models:\n", + "\n", + ":::{admonition} The Structure of the Document\n", + ":class: tip\n", + "\n", + "- Simulate data with known causal structure (including confounding and exclusion restrictions).\n", + "\n", + "- Fit and interpret Bayesian models for continuous treatments.\n", + "\n", + "- Extend to binary treatments and potential outcomes.\n", + "\n", + "- Use posterior predictive imputation to simulate counterfactuals.\n", + "\n", + "- Demonstrate the relationship between the structural modelling perspective with the potential outcomes framework.\n", + "\n", + "- Apply the model to an empircal example with parameter recovery checks and sensitivity analysis\n", + ":::\n", + "\n", + "\n", + "This approach will show how Bayesian methods provide a unified and transparent lens on causal inference. We will cover estimation, identification, and uncertainty in a single coherent framework. The goal is to demonstrate how model comparison and sensitivity analysis, core components of the contemporary Bayesian workflow, are uniquely suited to causal inference. By treating our models as probabilistic programs we can interrogate each under different assumptions, priors and specifications. The Bayesian framework of joint structural modelling makes transparent what most causal analyses leave implicit: which conclusions follow from data, and which follow from structural commitments.\n", + "\n", + "### Simulating the Source of Truth\n", + "\n", + "Every causal claim rests on untestable assumptions about the data-generating process. Before we can trust our methods in the wild, we must test them in controlled conditions where truth is known. The simulation below constructs such a laboratory: we specify the causal structure explicitly, introduce confounding deliberately, and then ask whether our Bayesian models recover what we seeded in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(123)\n", + "\n", + "\n", + "def inv_logit(z):\n", + " \"\"\"Compute the inverse logit (sigmoid) of z.\"\"\"\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "\n", + "def standardize_df(df, cols):\n", + " means = df[cols].mean()\n", + " sds = df[cols].std(ddof=1)\n", + " df_s = (df[cols] - means) / sds\n", + " return df_s, means, sds\n", + "\n", + "\n", + "def simulate_data(n=2500, alpha_true=3.0, rho=0.6, cate_estimation=False):\n", + " # Exclusion restrictions:\n", + " # X[0], X[1] affect both Y and T (confounders)\n", + " # X[2], X[3] affect ONLY T (instruments for T)\n", + " # X[4] affects ONLY Y (predictor of Y only)\n", + "\n", + " betaY = np.array([0.5, -0.3, 0.0, 0.0, 0.4, 0, 0, 0, 0]) # X[2], X[3] excluded\n", + " betaD = np.array([0.7, 0.1, -0.4, 0.3, 0.0, 0, 0, 0, 0]) # X[4] excluded\n", + " p = len(betaY)\n", + "\n", + " # noise variances and correlation\n", + " sigma_U = 3.0\n", + " sigma_V = 3.0\n", + "\n", + " # design matrix (n × p) with mean-zero columns\n", + " X = np.random.normal(size=(n, p))\n", + " X = (X - X.mean(axis=0)) / X.std(axis=0)\n", + "\n", + " mean = [0, 0]\n", + " cov = [[sigma_U**2, rho * sigma_U * sigma_V], [rho * sigma_U * sigma_V, sigma_V**2]]\n", + " errors = np.random.multivariate_normal(mean, cov, size=n)\n", + " U = errors[:, 0] # error in outcome equation\n", + " V = errors[:, 1] #\n", + "\n", + " # continuous treatment\n", + " T_cont = X @ betaD + V\n", + "\n", + " # latent variable for binary treatment\n", + " T_latent = X @ betaD + V\n", + " T_bin = np.random.binomial(n=1, p=inv_logit(T_latent), size=n)\n", + "\n", + " alpha_individual = 3.0 + 2.5 * X[:, 0]\n", + "\n", + " # outcomes\n", + " Y_cont = alpha_true * T_cont + X @ betaY + U\n", + " if cate_estimation:\n", + " Y_bin = alpha_individual * T_bin + X @ betaY + U\n", + " else:\n", + " Y_bin = alpha_true * T_bin + X @ betaY + U\n", + "\n", + " # combine into DataFrame\n", + " data = pd.DataFrame(\n", + " {\n", + " \"Y_cont\": Y_cont,\n", + " \"Y_bin\": Y_bin,\n", + " \"T_cont\": T_cont,\n", + " \"T_bin\": T_bin,\n", + " }\n", + " )\n", + " data[\"alpha\"] = alpha_true + alpha_individual\n", + " for j in range(p):\n", + " data[f\"feature_{j}\"] = X[:, j]\n", + " data[\"Y_cont_scaled\"] = (data[\"Y_cont\"] - data[\"Y_cont\"].mean()) / data[\n", + " \"Y_cont\"\n", + " ].std(ddof=1)\n", + " data[\"Y_bin_scaled\"] = (data[\"Y_bin\"] - data[\"Y_bin\"].mean()) / data[\"Y_bin\"].std(\n", + " ddof=1\n", + " )\n", + " data[\"T_cont_scaled\"] = (data[\"T_cont\"] - data[\"T_cont\"].mean()) / data[\n", + " \"T_cont\"\n", + " ].std(ddof=1)\n", + " data[\"T_bin_scaled\"] = (data[\"T_bin\"] - data[\"T_bin\"].mean()) / data[\"T_bin\"].std(\n", + " ddof=1\n", + " )\n", + " return data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each simulated observation has a treatment $T$, an outcome $Y$, and a set of covariates $X$ with distinct causal roles. Two covariates influence both the treatment and the outcome—these are the confounders. Two others affect only the treatment and serve as valid instruments. A final covariate affects only the outcome. The treatment and outcome errors are drawn from a correlated bivariate normal distribution, introducing endogeneity through their correlation parameter $\\rho$. When $\\rho$ is low the treatment can be considered exogenous and standard regression should recover the correct effect; while naive estimates will be biased when $\\rho$ is high.\n", + "\n", + "#### Confounding Structure\n", + "\n", + "The function produces both continuous and binary versions of the treatment and the outcome. This dual design lets us explore two worlds side by side: one where the treatment is a continuous dosage, and another where it is a binary decision. In both cases, the true causal effect of the treatment on the outcome is set to three. Because we know the truth, we can evaluate how well our Bayesian models recover true parameters. Even here you can see that the \"structure\" we impose on the world is abstraction over the concrete mechanisms acting in the world. We bundle the idea of selecting into the treatment as potential for correlation between treatment and outcome. This is a convenient and tractable proxy of a range of concrete settings where there is a risk of selection effects in the real world. \n", + "\n", + "![](../_static/JOINT_DAG.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the simulation code and the diagram above we have allowed the treatment and outcome to be predicted by shared variables `X0` and `X1`. These alone are sufficient to induce confounding into the estimation of the treatment on the outcome. We have also allowed `X2`, `X3` are potentially viable instrumental variables for predicting the outcome purged of the confounding effects of `X0` and `X1`. The rest of the variables are either noise or an independent predictor of the outcome. \n", + "\n", + "Before introducing the Bayesian machinery, it’s worth revisiting what goes wrong with ordinary least squares when the treatment and outcome share unobserved causes. The following code performs a simple sensitivity experiment: we vary the correlation $\\rho$ between the unobserved treatment and outcome errors and examine how the estimated treatment effect changes." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
treatment_effects_binarytreatment_effects_continuousrho
0-1.2017192.000000-1.000000
1-0.3511612.221169-0.777778
20.7664092.457946-0.555556
31.6446262.652292-0.333333
42.7120992.924260-0.111111
53.4411613.1175690.111111
64.3478163.3273240.333333
75.2434943.5374100.555556
86.2580623.7827070.777778
97.2487844.0000001.000000
\n", + "
" + ], + "text/plain": [ + " treatment_effects_binary treatment_effects_continuous rho\n", + "0 -1.201719 2.000000 -1.000000\n", + "1 -0.351161 2.221169 -0.777778\n", + "2 0.766409 2.457946 -0.555556\n", + "3 1.644626 2.652292 -0.333333\n", + "4 2.712099 2.924260 -0.111111\n", + "5 3.441161 3.117569 0.111111\n", + "6 4.347816 3.327324 0.333333\n", + "7 5.243494 3.537410 0.555556\n", + "8 6.258062 3.782707 0.777778\n", + "9 7.248784 4.000000 1.000000" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = simulate_data(n=2500, alpha_true=3, rho=0.6)\n", + "features = [col for col in data.columns if \"feature\" in col]\n", + "\n", + "treatment_effects_binary = []\n", + "treatment_effects_continuous = []\n", + "df_params = {\n", + " \"treatment_effects_binary\": [],\n", + " \"treatment_effects_continuous\": [],\n", + " \"rho\": [],\n", + "}\n", + "formula_cont = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n", + "formula_bin = \"Y_bin ~ T_bin + \" + \" + \".join(features)\n", + "for rho in np.linspace(-1, 1, 10):\n", + " data = simulate_data(n=2500, alpha_true=3, rho=rho)\n", + " model_cont = smf.ols(formula_cont, data=data).fit()\n", + " model_bin = smf.ols(formula_bin, data=data).fit()\n", + " df_params[\"treatment_effects_continuous\"].append(model_cont.params[\"T_cont\"])\n", + " df_params[\"treatment_effects_binary\"].append(model_bin.params[\"T_bin\"])\n", + " df_params[\"rho\"].append(rho)\n", + "\n", + "df_params = pd.DataFrame(df_params)\n", + "df_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This loop re-simulates the dataset ten times, each with a different value of $\\rho$, ranging from –1 to 1. For each dataset, it fits two OLS regressions: one for the continuous treatment, and another for the binary treatment, both controlling for all observed covariates. The estimated coefficient on the treatment variable `T_cont` or `T_bin`—represents what OLS believes to be the causal effect. By collecting these estimates in df_params, we can plot them against the true correlation to see how endogeneity distorts inference.\n", + "\n", + "When $\\rho = 0$ the treatment and outcome errors are independent, and OLS recovers the true causal effect of 3. But as $\\rho$ grows, the estimates drift away from the truth, sometimes dramatically. The direction of bias depends on the sign of the unobserved relationship. If hidden factors push both treatment and outcome the same way, OLS overstates the effect. If they act in opposite directions, it understates it. Even though we’ve controlled for all observed features, the unobserved correlation sneaks bias into our estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(20, 6))\n", + "ax.plot(\n", + " df_params[\"rho\"], df_params[\"treatment_effects_binary\"], label=\"Binary Treatment\"\n", + ")\n", + "ax.plot(\n", + " df_params[\"rho\"],\n", + " df_params[\"treatment_effects_continuous\"],\n", + " label=\"Continuous Treatment\",\n", + ")\n", + "ax.axhline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n", + "ax.set_xlabel(\"rho \\n correlation parameter\")\n", + "ax.set_ylabel(\"Treatment Effect Estimate\")\n", + "ax.set_title(\"Treatment Effect Estimates with OLS \\n Under Confounding\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now move from diagnosing bias to building a model that can recover causal effects under controlled conditions. To keep things interpretable, we begin with the unconfounded case, where the treatment and outcome share no latent correlation ($\\rho=0$). This setting lets us isolate what a Bayesian structural model actually does before we expose it to the challenges of endogeneity.\n", + "\n", + "#### Joint Modelling and Prior Structure\n", + "\n", + "At the heart of our approach is joint modelling: instead of fitting separate regressions for treatment and outcome, we model them together as draws from a joint multivariate distribution. The treatment equation captures how covariates predict exposure, while the outcome equation captures how both treatment and covariates predict the response. By expressing them jointly, we retain the covariance structure between their errors—an essential ingredient for causal inference once we later introduce confounding.\n", + "\n", + "The model is built using PyMC and organized through the function `make_joint_model()`. Each version shares the same generative logic but differs in how the priors handle variable selection and identification. We can think of these as different “dial settings” for how strongly the model shrinks irrelevant coefficients or searches for valid instruments. Four prior configurations are explored:\n", + "\n", + "- A normal prior, weak regularization with no variable selection. If the model succeeds here, the causal structure is identified through the joint modeling alone.\n", + "\n", + "- A spike-and-slab prior, which aggressively prunes away variables unlikely to matter, allowing the model to discover which features are true confounders or instruments.\n", + "\n", + "- A horseshoe prior, offering continuous shrinkage that downweights noise while preserving large signals. This is a middle path that downweights weak predictors without forcing them exactly to zero.\n", + "\n", + "- An exclusion-restriction prior, explicitly encoding which variables are allowed to influence the treatment but not the outcome, mimicking an instrumental-variable design.\n", + "\n", + "Each prior embodies a different epistemological stance on how much structure the data can learn versus how much the analyst must impose. In the unconfounded case, the treatment and outcome errors are independent, so the joint model effectively decomposes into two connected regressions. The treatment effect $\\alpha$ then captures the causal impact of the treatment on the outcome, and under this setting, its posterior should center around the true value of 3. The goal is not to solve confounding yet but to show that when the world is simple and well-behaved, the Bayesian model recovers the truth just as OLS does—but with richer uncertainty quantification and a coherent probabilistic structure.\n", + "\n", + "The following code defines the model and instantiates it under several prior choices. The model’s graphical representation, produced by `pm.model_to_graphviz()`, visualizes its structure: covariates feed into both the treatment and the outcome equations, the treatment coefficient $\\alpha$ links them, and the two residuals \n", + "$U$ and $V$ are connected through a correlation parameter $\\rho$, which we can freely set to zero or more substantive values. These parameterisations offer us a way to derive insight from the structure of the causal system under study. \n", + "\n", + "### Fitting the Continuous Treatment Model\n", + "\n", + "In this next code block we articulate the joint model for the continuous outcome and continuous treatment variable. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterbeta_outcome (9)\n", + "\n", + "beta_outcome (9)\n", + "\n", + "\n", + "cluster9\n", + "\n", + "9\n", + "\n", + "\n", + "clusterbeta_treatment (9)\n", + "\n", + "beta_treatment (9)\n", + "\n", + "\n", + "cluster2500 x 9\n", + "\n", + "2500 x 9\n", + "\n", + "\n", + "cluster2500 x 2\n", + "\n", + "2500 x 2\n", + "\n", + "\n", + "cluster2500\n", + "\n", + "2500\n", + "\n", + "\n", + "\n", + "pi_O\n", + "\n", + "pi_O\n", + "~\n", + "Beta\n", + "\n", + "\n", + "\n", + "gamma_O\n", + "\n", + "gamma_O\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "pi_O->gamma_O\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "rho\n", + "\n", + "rho\n", + "~\n", + "Uniform\n", + "\n", + "\n", + "\n", + "var_Y\n", + "\n", + "var_Y\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "rho->var_Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cov_YD\n", + "\n", + "cov_YD\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "rho->cov_YD\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cov_UV\n", + "\n", + "cov_UV\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "rho->cov_UV\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "likelihood\n", + "\n", + "likelihood\n", + "~\n", + "Multivariate_normal\n", + "\n", + "\n", + "\n", + "rho->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "h\n", + "\n", + "h\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "lower\n", + "\n", + "lower\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "h->lower\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "upper\n", + "\n", + "upper\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "h->upper\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "alpha\n", + "\n", + "alpha\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "alpha->var_Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "alpha->cov_YD\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "alpha->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_outcome\n", + "\n", + "mu_outcome\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "alpha->mu_outcome\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "lower->rho\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m\n", + "\n", + "m\n", + "~\n", + "Truncated_normal\n", + "\n", + "\n", + "\n", + "m->h\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m->lower\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "m->upper\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "var_D\n", + "\n", + "var_D\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "upper->rho\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_U\n", + "\n", + "sigma_U\n", + "~\n", + "Exponential\n", + "\n", + "\n", + "\n", + "sigma_U->var_Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_U->cov_YD\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_U->cov_UV\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_U->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "pi_T\n", + "\n", + "pi_T\n", + "~\n", + "Beta\n", + "\n", + "\n", + "\n", + "gamma_T\n", + "\n", + "gamma_T\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "pi_T->gamma_T\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_V\n", + "\n", + "sigma_V\n", + "~\n", + "Exponential\n", + "\n", + "\n", + "\n", + "sigma_V->var_D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_V->var_Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_V->cov_YD\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_V->cov_UV\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_V->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "s\n", + "\n", + "s\n", + "~\n", + "Beta\n", + "\n", + "\n", + "\n", + "s->h\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "gamma_O_u\n", + "\n", + "gamma_O_u\n", + "~\n", + "Uniform\n", + "\n", + "\n", + "\n", + "gamma_O_u->gamma_O\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_O_raw\n", + "\n", + "beta_O_raw\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "beta_O\n", + "\n", + "beta_O\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "beta_O_raw->beta_O\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_O->mu_outcome\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "gamma_O->beta_O\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_T\n", + "\n", + "beta_T\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "gamma_T->beta_T\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "gamma_T_u\n", + "\n", + "gamma_T_u\n", + "~\n", + "Uniform\n", + "\n", + "\n", + "\n", + "gamma_T_u->gamma_T\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_T_raw\n", + "\n", + "beta_T_raw\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "beta_T_raw->beta_T\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_treatment\n", + "\n", + "mu_treatment\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "beta_T->mu_treatment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X_data\n", + "\n", + "X_data\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "X_data->mu_treatment\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X_data->mu_outcome\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "observed\n", + "\n", + "observed\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "likelihood->observed\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_treatment->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_treatment->mu_outcome\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mu_outcome->likelihood\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coords = {\n", + " \"beta_outcome\": [col for col in data.columns if \"feature\" in col],\n", + " \"beta_treatment\": [col for col in data.columns if \"feature\" in col],\n", + " \"obs\": range(data.shape[0]),\n", + " \"latent\": [\"U\", \"V\"],\n", + " \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n", + " \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n", + "}\n", + "\n", + "\n", + "def relaxed_bernoulli(name, p, temperature=0.1, dims=None):\n", + " u = pm.Uniform(name + \"_u\", 0, 1, dims=dims)\n", + " logit_p = pt.log(p) - pt.log(1 - p)\n", + " return pm.Deterministic(\n", + " name, pm.math.sigmoid((logit_p + pt.log(u) - pt.log(1 - u)) / temperature)\n", + " )\n", + "\n", + "\n", + "def make_joint_model(X, Y, T, coords, priors_type=\"normal\", priors={}):\n", + " p = X.shape[1]\n", + " p0 = 5.0 # pick an expected number of nonzero coeffs\n", + " sigma_est = 1.0\n", + "\n", + " tau0 = (p0 / (p - p0)) * (sigma_est / np.sqrt(X.shape[0]))\n", + "\n", + " with pm.Model(coords=coords) as dml_model:\n", + " spike_and_slab = priors_type == \"spike_and_slab\"\n", + " horseshoe = priors_type == \"horseshoe\"\n", + " exclusion_restriction = priors_type == \"exclusion_restriction\"\n", + " p = X.shape[1]\n", + "\n", + " if not priors:\n", + " priors = {\n", + " \"rho\": [-0.99, 0.99],\n", + " }\n", + "\n", + " if spike_and_slab:\n", + " # RELAXED SPIKE-AND-SLAB PRIORS for aggressive variable selection\n", + "\n", + " pi_O = pm.Beta(\"pi_O\", alpha=2, beta=2)\n", + " beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n", + " gamma_O = relaxed_bernoulli(\n", + " \"gamma_O\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n", + " )\n", + " beta_outcome = pm.Deterministic(\n", + " \"beta_O\", gamma_O * beta_O_raw, dims=\"beta_outcome\"\n", + " )\n", + "\n", + " pi_T = pm.Beta(\"pi_T\", alpha=2, beta=2)\n", + " beta_T_raw = pm.Normal(\"beta_T_raw\", mu=0, sigma=2, dims=\"beta_treatment\")\n", + " gamma_T = relaxed_bernoulli(\n", + " \"gamma_T\", pi_T, temperature=0.1, dims=\"beta_treatment\"\n", + " )\n", + " beta_treatment = pm.Deterministic(\n", + " \"beta_T\", gamma_T * beta_T_raw, dims=\"beta_treatment\"\n", + " )\n", + "\n", + " elif horseshoe:\n", + " tau_O = pm.HalfStudentT(\"tau_O\", nu=3, sigma=tau0)\n", + " # Local shrinkage parameters (one per coefficient)\n", + " lambda_O = pm.HalfCauchy(\"lambda_O\", beta=1.0, dims=\"beta_outcome\")\n", + " # Regularized horseshoe: c² controls tail behavior\n", + " c2_O = pm.InverseGamma(\"c2_O\", alpha=2, beta=2)\n", + " lambda_tilde_O = pm.Deterministic(\n", + " \"lambda_tilde_O\",\n", + " pm.math.sqrt(c2_O * lambda_O**2 / (c2_O + tau_O**2 * lambda_O**2)),\n", + " dims=\"beta_outcome\",\n", + " )\n", + "\n", + " # Outcome coefficients with horseshoe prior\n", + " beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=1, dims=\"beta_outcome\")\n", + " beta_outcome = pm.Deterministic(\n", + " \"beta_O\", beta_O_raw * lambda_tilde_O * tau_O, dims=\"beta_outcome\"\n", + " )\n", + "\n", + " # Same for treatment equation\n", + " tau_T = pm.HalfStudentT(\"tau_T\", nu=3, sigma=tau0)\n", + " lambda_T = pm.HalfCauchy(\"lambda_T\", beta=1.0, dims=\"beta_treatment\")\n", + " c2_T = pm.InverseGamma(\"c2_T\", alpha=2, beta=2)\n", + " lambda_tilde_T = pm.Deterministic(\n", + " \"lambda_tilde_T\",\n", + " pm.math.sqrt(c2_T * lambda_T**2 / (c2_T + tau_T**2 * lambda_T**2)),\n", + " dims=\"beta_treatment\",\n", + " )\n", + "\n", + " beta_T_raw = pm.Normal(\"beta_T_raw\", mu=0, sigma=1, dims=\"beta_treatment\")\n", + " beta_treatment = pm.Deterministic(\n", + " \"beta_T\", beta_T_raw * lambda_tilde_T * tau_T, dims=\"beta_treatment\"\n", + " )\n", + " elif exclusion_restriction:\n", + " ### Ensuring that there is an instruments i.e. predictors of the treatment that\n", + " ### impact the outcome only through the treatment\n", + " beta_outcome = pm.Normal(\n", + " \"beta_O\",\n", + " 0,\n", + " [2.0, 2.0, 0.001, 0.001, 2.0, 2, 2, 2, 2],\n", + " dims=\"beta_outcome\",\n", + " )\n", + " beta_treatment = pm.Normal(\n", + " \"beta_T\",\n", + " 0,\n", + " [2.0, 2.0, 2.0, 2.0, 0.001, 2, 2, 2, 2],\n", + " dims=\"beta_treatment\",\n", + " )\n", + " else:\n", + " beta_outcome = pm.Normal(\"beta_O\", 0, 1, dims=\"beta_outcome\")\n", + " beta_treatment = pm.Normal(\"beta_T\", 0, 1, dims=\"beta_treatment\")\n", + "\n", + " X_data = pm.Data(\"X_data\", X.values)\n", + " observed_data = pm.Data(\"observed\", np.column_stack([Y.values, T.values]))\n", + "\n", + " alpha = pm.Normal(\"alpha\", mu=0, sigma=5)\n", + "\n", + " # Error standard deviations\n", + " sigma_U = pm.Exponential(\"sigma_U\", 1.0)\n", + " sigma_V = pm.Exponential(\"sigma_V\", 1.0)\n", + "\n", + " # Correlation between errors (confounding parameter)\n", + " m = pm.TruncatedNormal(\n", + " \"m\", mu=0, sigma=0.5, lower=priors[\"rho\"][0], upper=priors[\"rho\"][1]\n", + " )\n", + " s = pm.Beta(\"s\", 2, 2) # scaled half-width\n", + " h = pm.Deterministic(\"h\", s * (priors[\"rho\"][1] - pm.math.abs(m)))\n", + " lower = pm.Deterministic(\"lower\", m - h)\n", + " upper = pm.Deterministic(\"upper\", m + h)\n", + " rho = pm.Uniform(\"rho\", lower, upper)\n", + "\n", + " mu_treatment = pm.Deterministic(\"mu_treatment\", X_data @ beta_treatment)\n", + " mu_outcome = pm.Deterministic(\n", + " \"mu_outcome\", X_data @ beta_outcome + alpha * mu_treatment\n", + " )\n", + "\n", + " var_D = sigma_V**2\n", + " var_Y = alpha**2 * sigma_V**2 + sigma_U**2 + 2 * alpha * rho * sigma_U * sigma_V\n", + " cov_YD = alpha * sigma_V**2 + rho * sigma_U * sigma_V\n", + "\n", + " # Build 2x2 covariance matrix\n", + " cov = pm.math.stack([[var_Y, cov_YD], [cov_YD, var_D]])\n", + "\n", + " # Store as deterministic for inspection\n", + " _ = pm.Deterministic(\"var_Y\", var_Y)\n", + " _ = pm.Deterministic(\"var_D\", var_D)\n", + " _ = pm.Deterministic(\"cov_YD\", cov_YD)\n", + " _ = pm.Deterministic(\"cov_UV\", rho * sigma_U * sigma_V)\n", + "\n", + " mu = pm.math.stack([mu_outcome, mu_treatment], axis=1) # shape (n,2)\n", + " _ = pm.MvNormal(\"likelihood\", mu=mu, cov=cov, observed=observed_data)\n", + "\n", + " return dml_model\n", + "\n", + "\n", + "def make_continuous_models(data):\n", + " X = data[[col for col in data.columns if \"feature\" in col]]\n", + " Y = data[\"Y_cont\"]\n", + " T = data[\"T_cont\"]\n", + "\n", + " coords = {\n", + " \"beta_outcome\": [col for col in data.columns if \"feature\" in col],\n", + " \"beta_treatment\": [col for col in data.columns if \"feature\" in col],\n", + " \"obs\": range(data.shape[0]),\n", + " }\n", + "\n", + " spike_and_slab = make_joint_model(X, Y, T, coords, priors_type=\"spike_and_slab\")\n", + " horseshoe = make_joint_model(X, Y, T, coords, priors_type=\"horseshoe\")\n", + " excl = make_joint_model(X, Y, T, coords, priors_type=\"exclusion_restriction\")\n", + " normal = make_joint_model(X, Y, T, coords, priors_type=\"normal\")\n", + " tight_rho = make_joint_model(\n", + " X, Y, T, coords, priors_type=\"normal\", priors={\"rho\": [0.4, 0.99]}\n", + " )\n", + " tight_rho_s_s = make_joint_model(\n", + " X, Y, T, coords, priors_type=\"spike_and_slab\", priors={\"rho\": [0.4, 0.99]}\n", + " )\n", + "\n", + " models = {\n", + " \"spike_and_slab\": spike_and_slab,\n", + " \"horseshoe\": horseshoe,\n", + " \"exclusion\": excl,\n", + " \"normal\": normal,\n", + " \"tight_rho\": tight_rho,\n", + " \"tight_rho_s_s\": tight_rho_s_s,\n", + " }\n", + " return models\n", + "\n", + "\n", + "data_confounded = simulate_data(n=2500, alpha_true=3, rho=0.6)\n", + "data_unconfounded = simulate_data(n=2500, alpha_true=3, rho=0)\n", + "\n", + "models_confounded = make_continuous_models(data_confounded)\n", + "models_unconfounded = make_continuous_models(data_unconfounded)\n", + "\n", + "pm.model_to_graphviz(models_confounded[\"spike_and_slab\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This section orchestrates the fitting and sampling workflow for the suite of Bayesian models defined earlier. Having specified several variants of the joint outcome–treatment model—each differing only in its prior structure or treatment of the correlation parameter $\\rho$—we now turn to posterior inference.\n", + "\n", + "#### Various Model Specifications\n", + "\n", + "The functions `sample_model()`, and `fit_models()` provide a compact, repeatable sampling pipeline. Within the model context, it first draws from the prior predictive distribution, capturing what the model believes about the data before seeing any observations. These are comparable across each of models specified.\n", + "We're moving from describing how the data are assumed to arise, to actually learning from the simulated observations. This is the backwards inference step. The output `idata_unconfounded` contains all posterior draws, prior predictive samples, and posterior predictive simulations for every model variant under the assumption of no confounding. This will allow us to compare the inferences achieved under each setting. To gauge which are the most plausible parameterisations of the world-state conditioned on the data and our model-specification." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a68342e7df8e40ef9050860dae575879", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 366 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d38b0ccfeb94eadae3532124d96cda3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, c2_O, c2_T, lambda_O, lambda_T, likelihood, m, rho, s, sigma_U, sigma_V, tau_O, tau_T]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [tau_O, lambda_O, c2_O, beta_O_raw, tau_T, lambda_T, c2_T, beta_T_raw, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51ddee15310042e7b8c2a89bcaf7112b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 150 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "576017d153044af3ae5832cbbcadd850",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5a5f3f4c0ae443b7a90b6f098552ef60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 87 seconds.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c35adcbba5641eb82c6707490007351",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "969ad0f7158143628d2bde52db1665fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 120 seconds.\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "334f8fbe16ad446686d26fba7a47bbbf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d0c463b2e9e4cc986fe3498b295c8ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 85 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4de61de6eb6c4c9a90fecf70256af8b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c5f5491c40d46c287599b1411619eb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 385 seconds.\n",
+      "There were 7 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd670dbb62984c1692fa0ee147c58f48",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def sample_model(model, fit_kwargs):\n",
+    "    with model:\n",
+    "        idata = pm.sample_prior_predictive()\n",
+    "        idata.extend(\n",
+    "            pm.sample(\n",
+    "                draws=1000,\n",
+    "                tune=2000,\n",
+    "                target_accept=0.95,\n",
+    "                **fit_kwargs,\n",
+    "                idata_kwargs={\"log_likelihood\": True},\n",
+    "            )\n",
+    "        )\n",
+    "        idata.extend(pm.sample_posterior_predictive(idata))\n",
+    "    return idata\n",
+    "\n",
+    "\n",
+    "fit_kwargs = {}\n",
+    "\n",
+    "\n",
+    "def fit_models(fit_kwargs, models):\n",
+    "    idata_spike_and_slab = sample_model(models[\"spike_and_slab\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_horseshoe = sample_model(models[\"horseshoe\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_excl = sample_model(models[\"exclusion\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_normal = sample_model(models[\"normal\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_normal_rho_tight = sample_model(models[\"tight_rho\"], fit_kwargs=fit_kwargs)\n",
+    "    idata_rho_tight_s_s = sample_model(models[\"tight_rho_s_s\"], fit_kwargs=fit_kwargs)\n",
+    "\n",
+    "    idatas = {\n",
+    "        \"spike_and_slab\": idata_spike_and_slab,\n",
+    "        \"horseshoe\": idata_horseshoe,\n",
+    "        \"exclusion\": idata_excl,\n",
+    "        \"normal\": idata_normal,\n",
+    "        \"rho_tight\": idata_normal_rho_tight,\n",
+    "        \"rho_tight_spike_slab\": idata_rho_tight_s_s,\n",
+    "    }\n",
+    "\n",
+    "    return idatas\n",
+    "\n",
+    "\n",
+    "idata_unconfounded = fit_models(fit_kwargs, models_unconfounded)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before examining how different priors shape inference, it’s useful to clarify what our models are actually estimating. Each specification—spike-and-slab, horseshoe, exclusion restriction, and the others—ultimately targets the same estimand: the slope $\\alpha$ that captures how changes in the continuous treatment $T$ shift the expected outcome $Y$. In this setup, $\\alpha$ functions as a regression coefficient within the structural equation of our joint model. \n",
+    "\n",
+    "In econometric terms, what we’ve done so far sits squarely within the structural modelling tradition. We’ve written down a joint model for both the treatment and the outcome, specified their stochastic dependencies explicitly, and interpreted the slope $\\alpha$ as a structural parameter — a feature of the data-generating process itself. This parameter has a causal meaning only insofar as the model is correctly specified: if the structural form reflects how the world actually works, \n",
+    "$\\alpha$ recovers the true causal effect. By contrast, reduced-form econometrics focuses less on modelling the underlying mechanisms and more on identifying causal effects through observable associations research design — instrumental variables, difference-in-differences, or randomization. Reduced-form approaches avoid the need to specify the joint distribution of unobservables but often sacrifice interpretability: they estimate relationships that are valid for specific interventions or designs, not necessarily structural primitives.\n",
+    "\n",
+    "#### Comparing Treatment Estimates\n",
+    "\n",
+    "The comparison of models is a form of robustness checks. We want to inspect how consistent our parameter estimates are across different model specifications. Here we see how the strongly informative priors on $\\rho$ bias the treatment effect estimate.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_forest(\n",
+    "    [\n",
+    "        idata_unconfounded[\"spike_and_slab\"],\n",
+    "        idata_unconfounded[\"horseshoe\"],\n",
+    "        idata_unconfounded[\"exclusion\"],\n",
+    "        idata_unconfounded[\"normal\"],\n",
+    "        idata_unconfounded[\"rho_tight\"],\n",
+    "        idata_unconfounded[\"rho_tight_spike_slab\"],\n",
+    "    ],\n",
+    "    var_names=[\"alpha\", \"rho\", \"beta_O\", \"beta_T\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "    figsize=(20, 15),\n",
+    ")\n",
+    "\n",
+    "ax[0].axvline(3, linestyle=\"--\", color=\"k\")\n",
+    "ax[0].set_title(\n",
+    "    \"Comparing Parameter Estimates across Model Specifications\", fontsize=15\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the plot we can see that the majority of models accurately estimate the true treatment effect $\\alpha$ except in the cases where we have explicitly placed an opinionated prior on the $\\rho$ parameter in the model. These priors pull the $\\alpha$ estimate away from the true data generating process. The variable selection priors considerably shrink the uncertainty in the treatment estimates seemingly picking out the implicit instrument structure aping the application of instrumental variables. \n",
+    "\n",
+    "Our Bayesian setup here is intentionally structural. We specify how both treatment and outcome arise from common covariates and latent confounding structures. However, the boundary between structural and reduced-form reasoning becomes fluid when we begin to treat latent variables or exclusion restrictions as data-driven “instruments.” In that sense, the structural Bayesian approach can emulate reduced-form logic within a generative model — an idea we’ll develop further when we move from unconfounded to confounded data and later when we impute potential outcomes directly. \n",
+    "\n",
+    "But for now let's continue to examine the relationships between these structural parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
+    "axs = axs.flatten()\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"spike_and_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[0],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"horseshoe\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[1],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"exclusion\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[2],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"normal\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[3],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"rho_tight\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[4],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_unconfounded[\"rho_tight_spike_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[5],\n",
+    ")\n",
+    "for ax, m in zip(\n",
+    "    axs,\n",
+    "    [\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "):\n",
+    "    ax.axvline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n",
+    "    ax.axhline(0, linestyle=\"--\", color=\"red\", label=\"True rho\")\n",
+    "    ax.set_title(f\"Posterior Relationship {m}\")\n",
+    "    ax.legend(loc=\"lower left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Up to this point, we have looked at posterior summaries of individual parameters, such as the treatment effect $\\alpha$ or the correlation $\\rho$. While these marginal summaries are useful, they can obscure important interactions between parameters. In a structural model, the slope $\\alpha$ does not exist in isolation. Its interpretation depends on the joint distribution of the latent errors and the covariates that generate the treatment and outcome.\n",
+    "\n",
+    "The pairwise posterior plots below examine the joint distributions of $\\alpha$ and $\\rho$ and across different prior specifications. Each subplot shows the density of the posterior draws, highlighting how the inferred treatment effect co-varies with the estimated correlation between latent errors. The dashed vertical line marks the true causal effect, and the horizontal line shows the true values. \n",
+    "\n",
+    "By inspecting these joint distributions, we gain several insights: aggressive priors on $\\rho$ can pull the posterior of away from zero, which in turn shifts the distribution of the treatment effect estimate. But additionlly variable selection schemes like the spike-and-slab or horseshoe can significantly reduce uncertainty in the estimation of both $\\rho$ and $\\alpha$. This illustrates the trade-off between automated variable selection, prior specification. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
rho_tightalpha2.4590.2552.0052.9500.0100.008715.0752.01.00
rho0.4540.1670.1410.7590.0060.003722.0789.01.00
normalalpha3.0590.4172.3073.8590.0210.013388.0659.01.01
rho-0.0600.350-0.6760.5670.0180.007388.0643.01.01
spike_slabalpha3.0260.1482.7593.2600.0090.017678.0350.01.00
rho-0.0350.143-0.2860.2200.0070.011680.0344.01.00
horseshoealpha3.1330.2202.7323.5920.0110.011647.0289.01.00
rho-0.1330.197-0.5560.2140.0090.008651.0287.01.00
exclusion_restrictionalpha3.0180.1172.7843.2280.0020.0022277.01916.01.00
rho-0.0290.119-0.2500.1940.0030.0022238.01795.01.00
tight_rho_spike_slabalpha2.8290.1412.5843.0630.0070.011749.0342.01.01
rho0.1610.125-0.0660.3940.0060.007755.0346.01.01
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                              mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
+       "rho_tight             alpha  2.459  0.255   2.005    2.950      0.010   \n",
+       "                      rho    0.454  0.167   0.141    0.759      0.006   \n",
+       "normal                alpha  3.059  0.417   2.307    3.859      0.021   \n",
+       "                      rho   -0.060  0.350  -0.676    0.567      0.018   \n",
+       "spike_slab            alpha  3.026  0.148   2.759    3.260      0.009   \n",
+       "                      rho   -0.035  0.143  -0.286    0.220      0.007   \n",
+       "horseshoe             alpha  3.133  0.220   2.732    3.592      0.011   \n",
+       "                      rho   -0.133  0.197  -0.556    0.214      0.009   \n",
+       "exclusion_restriction alpha  3.018  0.117   2.784    3.228      0.002   \n",
+       "                      rho   -0.029  0.119  -0.250    0.194      0.003   \n",
+       "tight_rho_spike_slab  alpha  2.829  0.141   2.584    3.063      0.007   \n",
+       "                      rho    0.161  0.125  -0.066    0.394      0.006   \n",
+       "\n",
+       "                             mcse_sd  ess_bulk  ess_tail  r_hat  \n",
+       "rho_tight             alpha    0.008     715.0     752.0   1.00  \n",
+       "                      rho      0.003     722.0     789.0   1.00  \n",
+       "normal                alpha    0.013     388.0     659.0   1.01  \n",
+       "                      rho      0.007     388.0     643.0   1.01  \n",
+       "spike_slab            alpha    0.017     678.0     350.0   1.00  \n",
+       "                      rho      0.011     680.0     344.0   1.00  \n",
+       "horseshoe             alpha    0.011     647.0     289.0   1.00  \n",
+       "                      rho      0.008     651.0     287.0   1.00  \n",
+       "exclusion_restriction alpha    0.002    2277.0    1916.0   1.00  \n",
+       "                      rho      0.002    2238.0    1795.0   1.00  \n",
+       "tight_rho_spike_slab  alpha    0.011     749.0     342.0   1.01  \n",
+       "                      rho      0.007     755.0     346.0   1.01  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_params = pd.concat(\n",
+    "    {\n",
+    "        \"rho_tight\": az.summary(\n",
+    "            idata_unconfounded[\"rho_tight\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"normal\": az.summary(idata_unconfounded[\"normal\"], var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"spike_slab\": az.summary(\n",
+    "            idata_unconfounded[\"spike_and_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"horseshoe\": az.summary(\n",
+    "            idata_unconfounded[\"horseshoe\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"exclusion_restriction\": az.summary(\n",
+    "            idata_unconfounded[\"exclusion\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"tight_rho_spike_slab\": az.summary(\n",
+    "            idata_unconfounded[\"rho_tight_spike_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "df_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly we can compare the models on holistic performance measures like leave-one-out cross validation. Note however, that the primary purpose here is to showcase sensitivity of the parameter of interest to model specifications. We're not necessarily seeking to enshrine one model as the best. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df = az.compare(idata_unconfounded)\n",
+    "az.plot_compare(compare_df, figsize=(15, 7));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The tables highlights the model's sensitivity to priors. Sparse priors, like spike-and-slab and horseshoe, can slightly shrink coefficients and influence the posterior spread, particularly for $\\rho$, but strong priors directly on $\\rho$ can negatively impact the estimation routine. especially when there is no true correlation. This is not a flaw. It is a feature. In practical settings, treatments and outcomes are often correlated due to unobserved confounding, measurement error, or endogenous selection. For example, in a health economics study, patients who choose a particular therapy may do so because of unobserved health determinents that also influence recovery—such as risk tolerance, underlying severity, or access to informal support. In labor economics, higher wages may appear to cause greater job satisfaction, but workers who are more motivated or more socially connected might self-select into higher-paying jobs, creating correlation between the unobserved determinants of treatment and outcome By exposing the model to different prior assumptions, we can probe how strong beliefs about sparsity or instrument validity propagate into causal estimates.\n",
+    "\n",
+    "In other words, prior sensitivity is a diagnostic tool as much as a regularization mechanism. When $\\rho$ is expected to be nonzero i.e. in observational studies with likely latent confounding, then explicitly modelling its distribution becomes crucial. The unconfounded case, therefore, serves as a baseline: it confirms that our joint Bayesian model can recover true parameters when the world is simple, while setting the stage for exploring more realistic, confounded scenarios where these structural dependencies must be handled carefully."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Confounded Case\n",
+    "\n",
+    "While the unconfounded case provides a useful benchmark, most real-world observational studies involve some degree of endogenous treatment assignment. In our simulations, this occurs when the residuals of the treatment and outcome equations are correlated. This demonstrates that controlling only for measured variables is insufficient when unobserved confounders influence both treatment and outcome.\n",
+    "\n",
+    "The Bayesian joint model provides a principled solution. By explicitly modelling the correlation between treatment and outcome residuals, the framework can adjust for latent confounding while still estimating the causal slope $\\alpha$. Moreover, flexible priors such as spike-and-slab and horseshoe allow the model to automatically discover potential instruments i.e. covariates that predict the treatment but not the outcome. The theory is that the instrument structure if it holds in the world is also the one which best calibrates our parameters. These instruments help disentangle the structural effect of the treatment from latent correlations, improving identification.\n",
+    "\n",
+    "By setting $\\rho$ = 0.6 we simulate a moderate level of confounding—similar in spirit to cases where unmeasured preferences, abilities, or environmental factors drive both exposure and response. Conceptually, this setup mimics situations such as:\n",
+    "\n",
+    "-  More health-conscious individuals being both more likely to adopt a preventive therapy and more likely to recover quickly.\n",
+    "\n",
+    "- High-income households being more likely to invest in cleaner technologies and experience better environmental outcomes.\n",
+    "\n",
+    "- Firms with stronger internal capabilities both adopting new management practices and achieving higher productivity.\n",
+    "\n",
+    "Under such conditions, simple regression cannot disentangle correlation from causation, as the treatment is no longer independent of the unobserved outcome drivers.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "135082f391ce4af79931bc3c79375fd2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 476 seconds.\n",
+      "There were 9 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5049015de24438aa27d9226aa050e1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, c2_O, c2_T, lambda_O, lambda_T, likelihood, m, rho, s, sigma_U, sigma_V, tau_O, tau_T]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [tau_O, lambda_O, c2_O, beta_O_raw, tau_T, lambda_T, c2_T, beta_T_raw, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7161f7f9385421bbd9889b939b4b534",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 248 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7d6e0f8a9084749991137ce67285135",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df05ae550887422883aaae4af878c50c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 150 seconds.\n",
+      "There were 6 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "096317b5b934415095da3eb56c09fd0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c628bc7dbfab45adaf707c1356a45452",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 149 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5107e05dbbd4265a43d273530208d72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "478f7282aea84b51b40b36ea8babc098",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 99 seconds.\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "262ea3aa8da64fe4a4cb5ea5a5e6edff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82d95022a55047fd8c5858cfe9eab122",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 431 seconds.\n",
+      "There were 13 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [likelihood]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d97d1207b834493979adef88cfd851c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idata_confounded = fit_models(fit_kwargs, models_confounded)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can again compare these models on predictive performance measures, but the real focus is on the success of the causal identification within these model specifications. The performance metrics also highlight that they're broadly similar models. Another way to see this is that grading models on predictive performance does not ensure that the model correctly identifies the causal mechanism. Indistinguishable, predictive performance does not ensure mechanistic accuracy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df = az.compare(idata_confounded)\n",
+    "az.plot_compare(compare_df, figsize=(15, 8));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comparing Treatment Estimates\n",
+    "\n",
+    "The forest plot below compares posterior estimates of the treatment effect ($\\alpha$) and the confounding correlation ($\\rho$) across model specifications when \n",
+    "$\\rho = .6$ in the data-generating process. The baseline normal model (which places diffuse priors on all parameters) clearly reflects the presence of endogeneity. Its posterior mean for $\\alpha$ is biased upward relative to the true value of 3, and the estimated $\\rho$ is positive, confirming that the model detects correlation between treatment and outcome disturbances. This behaviour mirrors the familiar bias of OLS under confounding: without structural constraints or informative priors, the model attributes part of the outcome variation caused by unobserved factors to the treatment itself. This inflates and corrupts our treatment effect estimate. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_forest(\n",
+    "    [\n",
+    "        idata_confounded[\"spike_and_slab\"],\n",
+    "        idata_confounded[\"horseshoe\"],\n",
+    "        idata_confounded[\"exclusion\"],\n",
+    "        idata_confounded[\"normal\"],\n",
+    "        idata_confounded[\"rho_tight\"],\n",
+    "        idata_confounded[\"rho_tight_spike_slab\"],\n",
+    "    ],\n",
+    "    var_names=[\"alpha\", \"rho\", \"beta_O\", \"beta_T\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "    figsize=(20, 15),\n",
+    ")\n",
+    "\n",
+    "ax[0].axvline(3, linestyle=\"--\", color=\"k\")\n",
+    "ax[0].set_title(\n",
+    "    \"Comparing Parameter Estimates across Model Specifications\", fontsize=15\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By contrast, models that introduce structure through priors—either by tightening the prior range on $\\rho$ or imposing shrinkage on the regression coefficients—perform noticeably better. The tight-$\\rho$ models regularize the latent correlation, effectively limiting the extent to which endogeneity can distort inference, while spike-and-slab and horseshoe priors perform selective shrinkage on the covariates, allowing the model to emphasize variables that genuinely predict the treatment. This helps isolate more valid “instrument-like” components of variation, pulling the posterior of $\\alpha$ closer to the true causal effect. \n",
+    "\n",
+    "The exclusion-restriction specification, which enforces prior beliefs about which covariates affect only the treatment or only the outcome, performs well too. The imposed restrictions recover both the correct treatment effect and a tight estimate of residual correlation. It may be wishful thinking that this precise instrument structure is available to an analyst in the applied setting, but instrument variable designs and their imposed exclusion restrictions should be motivated by theory. Where that theory is plausible we can hope for such precise estimates.\n",
+    "\n",
+    "Together, these results illustrate the power of Bayesian joint modelling: even in the presence of confounding, appropriate prior structure enables partial recovery of causal effects. Importantly, the priors do not simply “fix” the bias—they make explicit the trade-offs between flexibility and identification. This transparency is one of the key advantages of Bayesian causal inference over traditional reduced-form methods.\n",
+    "\n",
+    "We can see similar patterns in the below pair plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
+    "axs = axs.flatten()\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"spike_and_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[0],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"horseshoe\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[1],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"exclusion\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[2],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"normal\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[3],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"rho_tight\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[4],\n",
+    ")\n",
+    "az.plot_pair(\n",
+    "    idata_confounded[\"rho_tight_spike_slab\"],\n",
+    "    var_names=[\"alpha\", \"rho\"],\n",
+    "    kind=\"kde\",\n",
+    "    divergences=True,\n",
+    "    ax=axs[5],\n",
+    ")\n",
+    "for ax, m in zip(\n",
+    "    axs,\n",
+    "    [\n",
+    "        \"spike_slab\",\n",
+    "        \"horse shoe\",\n",
+    "        \"exclusion_restriction\",\n",
+    "        \"normal\",\n",
+    "        \"tight_rho\",\n",
+    "        \"tight_rho_spike_slab\",\n",
+    "    ],\n",
+    "):\n",
+    "    ax.axvline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n",
+    "    ax.axhline(0.6, linestyle=\"--\", color=\"red\", label=\"True rho\")\n",
+    "    ax.set_title(f\"Posterior Relationship {m}\")\n",
+    "    ax.legend(loc=\"lower left\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each panel displays the joint posterior density between these two parameters for a given model specification.\n",
+    "\n",
+    "In the baseline normal model, the posteriors of $\\alpha$ and $\\rho$ exhibit a strong negative association: as the inferred residual correlation decreases, the estimated treatment effect increases. This pattern is characteristic of endogeneity. Part of the treatment’s apparent effect on the outcome is actually explained by unobserved factors that simultaneously drive both. The normal model correctly detects confounding but cannot disentangle its consequences without additional structure, leaving the treatment effect biased.\n",
+    "\n",
+    "One other feature evident from the spike and slab and horseshoe models is that the posterior distribution is somewhat bi-modal. The evidence pulls in two ways. There is not sufficient evidence in the data alone for the model to decisively characterise the $\\rho$ parameter and this induces a schizophrenic posterior distribution in the $\\alpha$ values estimated with these models. In other words, the posterior appears bi-modal. There are multiple centres of mass in the probability distribution representing a kind of indecision or oscillation between two views of the world.\n",
+    "\n",
+    "Introducing tight-$\\rho$ priors fundamentally changes this relationship. By constraining the allowable range of to moderate values, we effectively impose an analyst’s belief that the degree of confounding, while nonzero, is not overwhelming. This acts as a form of structural regularization: the posterior of $\\alpha$ stabilizes around the true causal effect. In practice, this mirrors what applied analysts often do implicitly. By imposing a weakly informative prior we anchor the model with plausible bounds on endogeneity rather than assuming perfect exogeneity or unbounded correlation. The preference for weakly informative priors here improves the sampling geometry but also clarifies the theoretical position of the analyst. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
rho_tightalpha3.0840.2632.5633.5180.0090.008842.01085.01.00
rho0.5120.1780.1870.8400.0060.003843.01076.01.00
normalalpha3.5540.3742.8784.2910.0180.011413.0727.01.01
rho0.0540.376-0.6500.6810.0180.008411.0763.01.01
spike_slabalpha3.3680.5072.5724.0130.0590.01193.0366.01.04
rho0.1820.469-0.4030.8180.0550.00593.0368.01.04
horseshoealpha3.4260.4052.6793.9770.0230.008337.0882.01.01
rho0.1590.396-0.3910.7860.0220.005339.0905.01.01
exclusion_restrictionalpha2.7630.1482.4763.0170.0040.0031498.01629.01.00
rho0.7190.0600.6090.8270.0020.0011503.01669.01.00
tight_rho_spike_slabalpha2.8630.2222.4653.2780.0080.004722.01524.01.00
rho0.6600.1160.4470.8550.0040.002725.01722.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                              mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
+       "rho_tight             alpha  3.084  0.263   2.563    3.518      0.009   \n",
+       "                      rho    0.512  0.178   0.187    0.840      0.006   \n",
+       "normal                alpha  3.554  0.374   2.878    4.291      0.018   \n",
+       "                      rho    0.054  0.376  -0.650    0.681      0.018   \n",
+       "spike_slab            alpha  3.368  0.507   2.572    4.013      0.059   \n",
+       "                      rho    0.182  0.469  -0.403    0.818      0.055   \n",
+       "horseshoe             alpha  3.426  0.405   2.679    3.977      0.023   \n",
+       "                      rho    0.159  0.396  -0.391    0.786      0.022   \n",
+       "exclusion_restriction alpha  2.763  0.148   2.476    3.017      0.004   \n",
+       "                      rho    0.719  0.060   0.609    0.827      0.002   \n",
+       "tight_rho_spike_slab  alpha  2.863  0.222   2.465    3.278      0.008   \n",
+       "                      rho    0.660  0.116   0.447    0.855      0.004   \n",
+       "\n",
+       "                             mcse_sd  ess_bulk  ess_tail  r_hat  \n",
+       "rho_tight             alpha    0.008     842.0    1085.0   1.00  \n",
+       "                      rho      0.003     843.0    1076.0   1.00  \n",
+       "normal                alpha    0.011     413.0     727.0   1.01  \n",
+       "                      rho      0.008     411.0     763.0   1.01  \n",
+       "spike_slab            alpha    0.011      93.0     366.0   1.04  \n",
+       "                      rho      0.005      93.0     368.0   1.04  \n",
+       "horseshoe             alpha    0.008     337.0     882.0   1.01  \n",
+       "                      rho      0.005     339.0     905.0   1.01  \n",
+       "exclusion_restriction alpha    0.003    1498.0    1629.0   1.00  \n",
+       "                      rho      0.001    1503.0    1669.0   1.00  \n",
+       "tight_rho_spike_slab  alpha    0.004     722.0    1524.0   1.00  \n",
+       "                      rho      0.002     725.0    1722.0   1.00  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_params = pd.concat(\n",
+    "    {\n",
+    "        \"rho_tight\": az.summary(\n",
+    "            idata_confounded[\"rho_tight\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"normal\": az.summary(idata_confounded[\"normal\"], var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"spike_slab\": az.summary(\n",
+    "            idata_confounded[\"spike_and_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"horseshoe\": az.summary(\n",
+    "            idata_confounded[\"horseshoe\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"exclusion_restriction\": az.summary(\n",
+    "            idata_confounded[\"exclusion\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"tight_rho_spike_slab\": az.summary(\n",
+    "            idata_confounded[\"rho_tight_spike_slab\"], var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "df_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Across all specifications, the diagnostics tell a consistent story: effective sample sizes are high, `rhat` values hover near 1.00, and divergent transitions are minimal or absent. These are healthy traces, suggesting that the posterior geometries are well-explored and that the models are numerically stable under their respective prior assumptions.\n",
+    "\n",
+    "### Causal Identification and Variable Selection\n",
+    "\n",
+    "Before continuing to the binary case it's worth diving into the role of priors in these structural causal models. Both spike and slab and horseshoe priors were designed to perform automatic variable selection. The spike-and-slab via a latent mixture of near-zero and freely estimated components, and the horseshoe through continuous shrinkage that allows strong predictors to survive while damping weak or spurious ones. Ultimately these priors determine the multiplicative weights of the $\\beta$ coefficients in the model. By placing these variable selection priors on the weights, they are calibrated against the data so as to zero-out those variables that are not required. For a more thorough discussion of automated variable selection using priors we recommend {cite:t}`kaplan_bs_social_science` and the [pymc discourse](https://discourse.pymc.io/t/question-on-how-to-model-spike-and-slab-priors/5277) site.\n",
+    "\n",
+    "Plotting these posteriors vividly illustrates their behavior more clearly than describing it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(\n",
+    "    idata_confounded[\"rho_tight_spike_slab\"], var_names=[\"beta_O\"], figsize=(20, 10)\n",
+    ")\n",
+    "plt.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This form of Bayesian regularization is crucial when the analyst suspects structural bias i.e. when some covariates may themselves be noise. By letting the model discover and downweight such variables, these priors act as a safeguard against overfitting endogenous structure. Bayesian variable selection is not merely a statistical convenience, but a structural choice about what relationships should be allowed to persist in the causal model. But this behavior should not be mistaken for a _magical salve_ for endogeneity. No prior, however clever, can know which variables are truly exogenous or which exclusion restrictions are defensible. That judgment must come from theory, domain expertise, and a careful causal design. \n",
+    "\n",
+    "Seen this way, these priors are best thought of as complements to theory, not substitutes for it. They are powerful tools for regularization and for exploring the robustness of our inferences, especially in high-dimensional or structurally ambiguous settings. Yet, they should always be deployed with a clear rationale about what the analyst believes to be the relevant sources of variation—and why."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Binary Treatment Case\n",
+    "\n",
+    "In practice, theory-driven variable selection tends to be more tractable when the treatment is binary. For instance, when a treatment represents a policy adoption, a clinical intervention, or a discrete decision - like entering a program or not. In such settings, the causal question is easier to articulate in design terms: What would have happened if this unit had not received the treatment? Because the intervention is categorical, analysts can often draw on institutional knowledge or policy mechanisms to reason about which variables are genuine confounders, which might serve as instruments, and which can be safely excluded. This clarity of design focus makes the binary treatment context an ideal laboratory for contrasting structural Bayesian modeling with the potential outcomes perspective.\n",
+    "\n",
+    "This also allows us to explore how Bayesian joint modeling connects to the potential outcomes framework, where causal effects are conceptualized not just as slopes in a regression, but as differences in counterfactual predictions.To explore this, we adapt our earlier joint modeling setup to the binary treatment context. The model below replaces the continuous treatment equation with a latent variable formulation that links predictors to a Bernoulli decision through a logistic transformation. The latent variables $U$ and $V$ introduce correlated residuals between the outcome and treatment equations, controlled by a correlation parameter $\\rho$. This setup captures endogenous selection into the treatment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "cluster2500 x 9\n",
+       "\n",
+       "2500 x 9\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "clusterbeta_treatment (9)\n",
+       "\n",
+       "beta_treatment (9)\n",
+       "\n",
+       "\n",
+       "clusterbeta_outcome (9)\n",
+       "\n",
+       "beta_outcome (9)\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data\n",
+       "\n",
+       "X_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome\n",
+       "\n",
+       "mu_outcome\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment\n",
+       "\n",
+       "mu_treatment\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_outcome\n",
+       "\n",
+       "likelihood_outcome\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "y_data\n",
+       "\n",
+       "y_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_outcome->y_data\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_treatment\n",
+       "\n",
+       "likelihood_treatment\n",
+       "~\n",
+       "Bernoulli\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data\n",
+       "\n",
+       "t_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_treatment->t_data\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment->likelihood_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha\n",
+       "\n",
+       "alpha\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr\n",
+       "\n",
+       "rho_unconstr\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho\n",
+       "\n",
+       "rho\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr->rho\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps\n",
+       "\n",
+       "eps\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U\n",
+       "\n",
+       "sigma_U\n",
+       "~\n",
+       "Halfnormal\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw\n",
+       "\n",
+       "eps_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T\n",
+       "\n",
+       "beta_T\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O\n",
+       "\n",
+       "beta_O\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_confounded = simulate_data(n=2500, alpha_true=3, rho=0.6, cate_estimation=True)\n",
+    "\n",
+    "\n",
+    "coords = {\n",
+    "    \"beta_outcome\": [col for col in data_unconfounded.columns if \"feature\" in col],\n",
+    "    \"beta_treatment\": [col for col in data_unconfounded.columns if \"feature\" in col],\n",
+    "    \"obs\": range(data_unconfounded.shape[0]),\n",
+    "    \"latent\": [\"U\", \"V\"],\n",
+    "    \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n",
+    "    \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def make_binary_model(\n",
+    "    data,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    bart_outcome=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=None,\n",
+    "    Y=None,\n",
+    "    T=None,\n",
+    "    priors=None,\n",
+    "    observed=True,\n",
+    "    spike_and_slab=False,\n",
+    "):\n",
+    "    if X is None:\n",
+    "        X = data[[col for col in data.columns if \"feature\" in col]]\n",
+    "        Y = data[\"Y_bin\"].values\n",
+    "        T = data[\"T_bin\"].values\n",
+    "\n",
+    "    if priors is None:\n",
+    "        priors = {\n",
+    "            \"rho\": [0, 0.5],\n",
+    "            \"alpha\": [0, 10],\n",
+    "            \"beta_O\": [0, 1],\n",
+    "            \"eps\": [0, 1],\n",
+    "            \"sigma_U\": [1],\n",
+    "        }\n",
+    "\n",
+    "    with pm.Model(coords=coords) as binary_model:\n",
+    "        X_data = pm.Data(\"X_data\", X.values)\n",
+    "        y_data = pm.Data(\"y_data\", Y)\n",
+    "        t_data = pm.Data(\"t_data\", T)\n",
+    "\n",
+    "        alpha = pm.Normal(\"alpha\", priors[\"alpha\"][0], priors[\"alpha\"][1])\n",
+    "        sigma_U = pm.HalfNormal(\"sigma_U\", priors[\"sigma_U\"][0])\n",
+    "        # just correlation, not full covariance\n",
+    "\n",
+    "        rho_unconstr = pm.Normal(\"rho_unconstr\", priors[\"rho\"][0], priors[\"rho\"][1])\n",
+    "        rho = pm.Deterministic(\"rho\", pm.math.tanh(rho_unconstr))  # keep |rho|<1\n",
+    "\n",
+    "        inverse_rho = pm.math.sqrt(pm.math.maximum(1 - rho**2, 1e-12))\n",
+    "        chol = pt.stack([[sigma_U, 0.0], [sigma_U * rho, inverse_rho]])\n",
+    "\n",
+    "        # --- Draw latent errors ---\n",
+    "        eps_raw = pm.Normal(\n",
+    "            \"eps_raw\", priors[\"eps\"][0], priors[\"eps\"][1], shape=(len(data), 2)\n",
+    "        )\n",
+    "        eps = pm.Deterministic(\"eps\", pt.dot(eps_raw, chol.T))\n",
+    "\n",
+    "        U = eps[:, 0]\n",
+    "        V = eps[:, 1]\n",
+    "\n",
+    "        if bart_treatment:\n",
+    "            mu_treatment = pmb.BART(\"mu_treatment_bart\", X=X_data, Y=t_data) + V\n",
+    "        else:\n",
+    "            beta_treatment = pm.Normal(\"beta_T\", 0, 1, dims=\"beta_treatment\")\n",
+    "            mu_treatment = pm.Deterministic(\n",
+    "                \"mu_treatment\", (X_data @ beta_treatment) + V\n",
+    "            )\n",
+    "        p_t = pm.math.invlogit(mu_treatment)\n",
+    "        if observed:\n",
+    "            _ = pm.Bernoulli(\"likelihood_treatment\", p_t, observed=t_data)\n",
+    "        else:\n",
+    "            _ = pm.Bernoulli(\"likelihood_treatment\", p_t)\n",
+    "\n",
+    "        if cate_estimation:\n",
+    "            pi_O = pm.Beta(\"pi_O\", alpha=2, beta=2)\n",
+    "            alpha_O_raw = pm.Normal(\"alpha_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n",
+    "            gamma_O = relaxed_bernoulli(\n",
+    "                \"gamma_O\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n",
+    "            )\n",
+    "            alpha_interaction_outcome = pm.Deterministic(\n",
+    "                \"alpha_interact\", gamma_O * alpha_O_raw, dims=\"beta_outcome\"\n",
+    "            )\n",
+    "            alpha = alpha + pm.math.dot(X_data, alpha_interaction_outcome)\n",
+    "\n",
+    "        if bart_outcome:\n",
+    "            mu_outcome = pmb.BART(\"mu_outcome_bart\", X=X_data, Y=y_data) + U\n",
+    "        else:\n",
+    "            if spike_and_slab:\n",
+    "                pi_O = pm.Beta(\"pi_O_b\", alpha=2, beta=2)\n",
+    "                beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n",
+    "                gamma_O = relaxed_bernoulli(\n",
+    "                    \"gamma_O_b\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n",
+    "                )\n",
+    "                beta_outcome = pm.Deterministic(\"beta_O\", gamma_O * beta_O_raw)\n",
+    "                mu_outcome = pm.Deterministic(\n",
+    "                    \"mu_outcome\", (X_data @ beta_outcome) + alpha * t_data + U\n",
+    "                )\n",
+    "            else:\n",
+    "                beta_outcome = pm.Normal(\n",
+    "                    \"beta_O\",\n",
+    "                    priors[\"beta_O\"][0],\n",
+    "                    priors[\"beta_O\"][1],\n",
+    "                    dims=\"beta_outcome\",\n",
+    "                )\n",
+    "                mu_outcome = pm.Deterministic(\n",
+    "                    \"mu_outcome\", (X_data @ beta_outcome) + alpha * t_data + U\n",
+    "                )\n",
+    "\n",
+    "        if observed:\n",
+    "            _ = pm.Normal(\n",
+    "                \"likelihood_outcome\", mu_outcome, sigma=sigma_U, observed=y_data\n",
+    "            )\n",
+    "        else:\n",
+    "            _ = pm.Normal(\"likelihood_outcome\", mu_outcome, sigma=sigma_U)\n",
+    "\n",
+    "    return binary_model\n",
+    "\n",
+    "\n",
+    "binary_model_bart_treatment = make_binary_model(\n",
+    "    data_confounded, coords, bart_treatment=True\n",
+    ")\n",
+    "binary_model_bart_treatment_cate = make_binary_model(\n",
+    "    data_confounded, coords, bart_treatment=True, cate_estimation=True\n",
+    ")\n",
+    "binary_model = make_binary_model(data_confounded, coords)\n",
+    "binary_model_bart_outcome = make_binary_model(\n",
+    "    data_confounded, coords, bart_outcome=True\n",
+    ")\n",
+    "pm.model_to_graphviz(binary_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The nested dependency structure of the model can be seen clearly in the graph above. In the binary setting, the, $\\alpha$ parameter captures the average difference in outcomes between treated and untreated units, but as before we are aiming to capture a treatment effect estimate of 3. This model is still bivariate normal in that the latent draws of `eps_raw` are transformed to reflect the correlation encoded in $\\rho$. \n",
+    "\n",
+    "$$\n",
+    "\\epsilon_{\\text{raw}, i} =\n",
+    "\\begin{pmatrix} \\epsilon_{U,i}^{\\text{raw}} \\ \\epsilon_{V,i}^{\\text{raw}} \\end{pmatrix}\n",
+    "\\sim \\mathcal{N}\\left(\\begin{pmatrix} 0 \\ 0 \\end{pmatrix}, \\mathbf{I}_2\\right)\n",
+    "$$\n",
+    "\n",
+    "due to the dot product multiplication\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "\\begin{pmatrix} U_i \\ V_i \\end{pmatrix} = \\mathbf{chol} \\cdot \\epsilon_{\\text{raw}, i} \\sim \\mathcal{N}\\left(\n",
+    "\\begin{pmatrix} 0 \\ 0 \\end{pmatrix},\n",
+    "\\mathbf{\\Sigma} \\right)\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "This is a convenient representation for the bivariate binary case that samples quite efficiently. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, eps_raw, likelihood_outcome, likelihood_treatment, mu_treatment_bart, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_O]\n",
+      ">PGBART: [mu_treatment_bart]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9ec2107fc1340c6a9b5b976b9bedd74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 99 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f56a0f6b11424be48fbd562b2a2231e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 28 seconds.\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, mu_outcome_bart, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T]\n",
+      ">PGBART: [mu_outcome_bart]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f17f95bc504f416b88724c3c81a2cdbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 96 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, alpha_O_raw, beta_O, eps_raw, gamma_O_u, likelihood_outcome, likelihood_treatment, mu_treatment_bart, pi_O, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, pi_O, alpha_O_raw, gamma_O_u, beta_O]\n",
+      ">PGBART: [mu_treatment_bart]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02a104c5d8cd4cf4a823cefcbaf24ed1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 255 seconds.\n",
+      "There were 20 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fit_binary_model(model):\n",
+    "    with model:\n",
+    "        idata = pm.sample_prior_predictive()\n",
+    "        idata.extend(pm.sample(target_accept=0.95))\n",
+    "    return idata\n",
+    "\n",
+    "\n",
+    "idata_binary_model_bart_treatment = fit_binary_model(binary_model_bart_treatment)\n",
+    "idata_binary_model = fit_binary_model(binary_model)\n",
+    "idata_binary_bart_outcome = fit_binary_model(binary_model_bart_outcome)\n",
+    "idata_binary_bart_treatment_cate = fit_binary_model(binary_model_bart_treatment_cate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comparing Treatment Estimates\n",
+    "\n",
+    "Three of our four approaches successfully recover the true causal effect of 3.0, with tight uncertainty bands and accurate confounding estimates. But when BART enters the outcome equation, the results collapse: the treatment effect estimate drops to near-zero. This is not a sampling failure. Diagnostics show healthy chains, good ESS, and converged r-hat values. The model is doing exactly what we asked it to do. The problem is what we asked the model to do!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 2, figsize=(20, 6), sharex=True)\n",
+    "axs = axs.flatten()\n",
+    "az.plot_posterior(idata_binary_model_bart_treatment, var_names=\"alpha\", ax=axs[0])\n",
+    "az.plot_posterior(idata_binary_bart_outcome, var_names=\"alpha\", ax=axs[1])\n",
+    "az.plot_posterior(idata_binary_model, var_names=\"alpha\", ax=axs[2])\n",
+    "az.plot_posterior(idata_binary_bart_treatment_cate, var_names=\"alpha\", ax=axs[3])\n",
+    "for ax, title in zip(\n",
+    "    axs, [\"bart_treatment\", \"bart_outcome\", \"no_bart\", \"bart_treatment_cate\"]\n",
+    "):\n",
+    "    ax.axvline(3, linestyle=\"--\", color=\"k\")\n",
+    "    ax.set_title(f\"Model: {title}\")\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The failure stems from a fundamental tension between flexibility and causal identification. In our data generating process the treatment is strongly predicted by the covariates. The flexibility of the BART outcome model picks up on this pattern. It learns the total association and does not distinguish causal relationships from association. When we then add a structural parameter α for the treatment effect, we're asking: what is the effect of the treatment _after_ BART has already explained outcome variation using the treatment predictive features. We can see this reflected in the $\\rho$ parameter for the BART outcome model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
linear_no_bartalpha3.5340.1243.3083.7710.0060.002457.01184.01.01
rho0.5370.0550.4300.6330.0030.001379.01102.01.01
bart_treatmentalpha3.5680.1303.3333.8170.0060.003461.01105.01.01
rho0.5170.0550.4120.6230.0030.001378.0756.01.01
bart_outcomealpha-0.06410.005-18.67418.9120.1110.1678168.02691.01.00
rho0.9740.0110.9540.9920.0000.0003504.03416.01.00
bart_treatment_catealpha3.2310.1133.0203.4380.0050.002461.0776.01.01
rho0.7490.0610.6320.8620.0030.001357.0831.01.01
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                            mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "linear_no_bart      alpha  3.534   0.124   3.308    3.771      0.006    0.002   \n",
+       "                    rho    0.537   0.055   0.430    0.633      0.003    0.001   \n",
+       "bart_treatment      alpha  3.568   0.130   3.333    3.817      0.006    0.003   \n",
+       "                    rho    0.517   0.055   0.412    0.623      0.003    0.001   \n",
+       "bart_outcome        alpha -0.064  10.005 -18.674   18.912      0.111    0.167   \n",
+       "                    rho    0.974   0.011   0.954    0.992      0.000    0.000   \n",
+       "bart_treatment_cate alpha  3.231   0.113   3.020    3.438      0.005    0.002   \n",
+       "                    rho    0.749   0.061   0.632    0.862      0.003    0.001   \n",
+       "\n",
+       "                           ess_bulk  ess_tail  r_hat  \n",
+       "linear_no_bart      alpha     457.0    1184.0   1.01  \n",
+       "                    rho       379.0    1102.0   1.01  \n",
+       "bart_treatment      alpha     461.0    1105.0   1.01  \n",
+       "                    rho       378.0     756.0   1.01  \n",
+       "bart_outcome        alpha    8168.0    2691.0   1.00  \n",
+       "                    rho      3504.0    3416.0   1.00  \n",
+       "bart_treatment_cate alpha     461.0     776.0   1.01  \n",
+       "                    rho       357.0     831.0   1.01  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat(\n",
+    "    {\n",
+    "        \"linear_no_bart\": az.summary(idata_binary_model, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"bart_treatment\": az.summary(\n",
+    "            idata_binary_model_bart_treatment, var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"bart_outcome\": az.summary(\n",
+    "            idata_binary_bart_outcome, var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "        \"bart_treatment_cate\": az.summary(\n",
+    "            idata_binary_bart_treatment_cate, var_names=[\"alpha\", \"rho\"]\n",
+    "        ),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `bart_outcome` model places weight on the correlation between treatment and outcome rather than parcel out the share of impact into the treatment and confounding relationship. The causal effect absorbed into the covariate adjustment of the BART component, and we have a fundamental misattribution which makes recovery of structural parameter impossible in this set up. The other two BART model specifications; `bart_treatment` and `bart_treatment_cate` correctly identify the structural parameter because the BART component is used to flexibly model the treatment status. The structural parameter $\\alpha$ remains identifiable as the average or baseline effect because we've partialied out the variation in the outcome explicitly. The more traditional `linear_no_bart` model does not have the flexibility to absorb the causal effect into a non-linear component. As such, the structural parameter remains identifiable. This is one of the virtues of \"simpler\" models. \n",
+    "\n",
+    "### Non-Parametric Causal Inference\n",
+    "\n",
+    "We might worry that these parametric approaches to identifying causal effects hide the real lesson. Non-parametric approximation functions can still learn the correct expected value function and we ought to derive causal estimates via the imputation of potential outcomes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](../_static/probabilistic_intervention_fix.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should verify that the BART-outcome model's failure isn't merely a problem with how we've extracted the treatment effect parameter $\\alpha$. Perhaps the structural parameter collapsed, but the model could still recover causal effects through direct counterfactual imputation. Rather than interpreting a regression coefficient, we directly simulate potential outcomes:\n",
+    "\n",
+    "- Fit a model for $E[Y | X, T]$ (however flexible)\n",
+    "- Impute $Y(1)$: Set everyone to treated, predict outcomes\n",
+    "- Impute $Y(0)$: Set everyone to control, predict outcomes\n",
+    "- Compute ATE: Average the difference $Y(1) - Y(0)$\n",
+    "\n",
+    "This approach is appealing because it doesn't require interpreting structural parameters. If the model has learned the correct conditional expectation function, counterfactual imputation should recover the true causal effect—even if $\\alpha$ itself is uninterpretable. This process of imputation is then repeated across many, many samples to derive the posterior distribution of the treatment effect. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0dac600f1c19409a90a5c4b566ed0dc9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6635c4961b4474b8cf4e1c2d9fff780",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de4b7bd0d2e34260b33cddcbba7dbb45",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 3.5664742872703017\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95eee65bc0654aaaa589c66f500032d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70988452609d48a9945db45fc402e1c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 0.0025937802541776207\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93dba638f9a84d99839cd8dfd7494322",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20728f6fb8e54059bce2e4cb6ed2ed9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 3.534009004071455\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [likelihood_outcome]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4bb2a8b4f74a4107921ceeecd26b62c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Imputed Difference in Potential Outcomes 3.2317211079609693\n"
+     ]
+    }
+   ],
+   "source": [
+    "def impute_potential_outcomes(model, idata, n=2500):\n",
+    "    with model:\n",
+    "        # Posterior predictive under treatment\n",
+    "        pm.set_data({\"t_data\": np.ones(n, dtype=\"int\")})\n",
+    "        Y1 = pm.sample_posterior_predictive(idata, var_names=[\"likelihood_outcome\"])\n",
+    "\n",
+    "        # Posterior predictive under control\n",
+    "        pm.set_data({\"t_data\": np.zeros(n, dtype=\"int\")})\n",
+    "        Y0 = pm.sample_posterior_predictive(idata, var_names=[\"likelihood_outcome\"])\n",
+    "        ATE = (\n",
+    "            Y1[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "            - Y0[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "        ).mean()\n",
+    "        print(\"Imputed Difference in Potential Outcomes\", ATE.item())\n",
+    "    return Y1, Y0, ATE.item()\n",
+    "\n",
+    "\n",
+    "y1_bart_treatment, y0_bart_treatment, ate_bart_treatment = impute_potential_outcomes(\n",
+    "    binary_model_bart_treatment, idata_binary_model_bart_treatment\n",
+    ")\n",
+    "\n",
+    "y1_bart_outcome, y0_bart_outcome, ate_bart_outcome = impute_potential_outcomes(\n",
+    "    binary_model_bart_outcome, idata_binary_bart_outcome\n",
+    ")\n",
+    "\n",
+    "y1_no_bart, y0_no_bart, ate_linear = impute_potential_outcomes(\n",
+    "    binary_model, idata_binary_model\n",
+    ")\n",
+    "\n",
+    "y1_treatment_cate, y0_treatment_cate, ate_cate = impute_potential_outcomes(\n",
+    "    binary_model_bart_treatment_cate, idata_binary_bart_treatment_cate\n",
+    ")\n",
+    "\n",
+    "imputed_effects = pd.DataFrame(\n",
+    "    {\n",
+    "        \"model\": [\n",
+    "            \"bart_treatment\",\n",
+    "            \"bart_outcome\",\n",
+    "            \"linear_no_bart\",\n",
+    "            \"bart_treatment_cate\",\n",
+    "        ],\n",
+    "        \"ate\": [ate_bart_treatment, ate_bart_outcome, ate_linear, ate_cate],\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above code we have applied the following process to impute potential outcomes for each individual under different treatment regimes. \n",
+    "\n",
+    "![](../_static/potential_outcomes.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are striking in their consistency. For the three successful specifications, both methods of extracting causal effects agree. For the `bart_outcome` specification the Imputation approach to causal inference also fails. This is crucial. The failure is not about how we interrogate the model, but about what the model learned during fitting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
modelate
0bart_treatment3.566474
1bart_outcome0.002594
2linear_no_bart3.534009
3bart_treatment_cate3.231721
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                 model       ate\n",
+       "0       bart_treatment  3.566474\n",
+       "1         bart_outcome  0.002594\n",
+       "2       linear_no_bart  3.534009\n",
+       "3  bart_treatment_cate  3.231721"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputed_effects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In prediction tasks, BART's flexibility is a pure advantage. It finds patterns we didn't know to look for, captures complex interactions automatically, and often achieves superior out-of-sample accuracy. But in causal inference, this same flexibility becomes a liability when it absorbs the variation we're trying to causally attribute. The problem is **structural**: any sufficiently flexible method faces this challenge. Methods that can perfectly adapt their functional form to training data will inadvertently learn causal pathways as associational patterns, unless the structure learning is constrained to partial out the treatment influences. The stronger the relationship between the predictors of the outcome and the treatment, the more we can expect to see this collapse. Flexible outcome modelling may be useful in cases where the relationship between treatment and covariates is truly independent, but it presents a risk where the focus is on recovering treatment effects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Conditional Average Treatment Effects\n",
+    "\n",
+    "The BART-treatment model demonstrated that flexibility in the treatment equation doesn't harm identification. We can also introduce flexibility in how treatment effects vary with covariates, while preserving the interpretability and identifiability of structural parameters? Our `bart_treatment_cate` model allows this by interacting the treatment parameter with the covariates. This explicitly parameterize effect heterogeneity. Unlike BART in the outcome equation (which failed because it absorbed the entire treatment signal), interaction terms allow treatment effects to vary while retaining a structural interpretation. This allows flexibility while retaining identifiability. \n",
+    "\n",
+    "We can see this flexibility by pulling out the ITE (individual treatment effects) estimates, using the potential outcomes imputations. We can compare the ITEs across the `bart_treatment_cate` and `linear_no_bart` models. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cate = (\n",
+    "    y1_treatment_cate[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "    - y0_treatment_cate[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "sample_cate = (\n",
+    "    cate.mean(dim=(\"chain\", \"draw\"))\n",
+    "    .to_dataframe()\n",
+    "    .sample(100)\n",
+    "    .sort_values(\"likelihood_outcome\")\n",
+    ")\n",
+    "\n",
+    "cate_linear = (\n",
+    "    y1_no_bart[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    "    - y0_no_bart[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
+    ")\n",
+    "\n",
+    "res = smf.ols(\n",
+    "    \"\"\"Y_bin ~ T_bin + feature_0 + feature_1 + feature_2 + feature_3 + feature_4 + feature_5 + feature_6 + feature_7 + feature_8\"\"\",\n",
+    "    data,\n",
+    ").fit()\n",
+    "ols_est = res.params[\"T_bin\"]\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(15, 20))\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "ax = az.plot_forest(\n",
+    "    cate_linear,\n",
+    "    combined=True,\n",
+    "    figsize=(15, 15),\n",
+    "    coords={\"likelihood_outcome_dim_0\": sample_cate.index},\n",
+    "    ax=axs[0],\n",
+    ")\n",
+    "axs[0].axvline(3, color=\"red\", label=\"True Treatment Effect\")\n",
+    "axs[0].axvline(ols_est, color=\"darkgreen\", label=\"OLS Estimate\")\n",
+    "axs[0].legend()\n",
+    "axs[0].set_title(\"ITE Linear-Model estimates \\n Random Sample of 100\")\n",
+    "\n",
+    "ax = az.plot_forest(\n",
+    "    cate,\n",
+    "    combined=True,\n",
+    "    figsize=(15, 10),\n",
+    "    coords={\"likelihood_outcome_dim_0\": sample_cate.index},\n",
+    "    ax=axs[1],\n",
+    ")\n",
+    "axs[1].axvline(3, color=\"red\", label=\"True Treatment Effect\")\n",
+    "axs[1].axvline(ols_est, color=\"darkgreen\", label=\"OLS Estimate\")\n",
+    "axs[1].set_title(\"ITE CATE-Model estimates \\n Random Sample of 100\")\n",
+    "\n",
+    "n = len(sample_cate.index)\n",
+    "n_order = range(n, 0, -1)\n",
+    "axs[1].scatter(data_confounded.iloc[sample_cate.index][\"alpha\"], n_order, color=\"black\")\n",
+    "axs[1].legend()\n",
+    "plt.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This comparison shows how the flexibility of treatment effects can be incorporated without losing interpretability. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### An Empirical Application\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now explore an example with a real data set from the NHEFS study about the effects of quitting smoking on weight. Ultimately we will compare our estimates of treatment effects to a well specified and sensible regression model. The goal is not to provide the regression estimate as a source of truth, but to show the reasonable diversity of views we can achieve while aiming to estimate causal impact. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "cluster1566 x 14\n",
+       "\n",
+       "1566 x 14\n",
+       "\n",
+       "\n",
+       "cluster1566\n",
+       "\n",
+       "1566\n",
+       "\n",
+       "\n",
+       "cluster1566 x 2\n",
+       "\n",
+       "1566 x 2\n",
+       "\n",
+       "\n",
+       "clusterbeta_treatment (14)\n",
+       "\n",
+       "beta_treatment (14)\n",
+       "\n",
+       "\n",
+       "clusterbeta_outcome (14)\n",
+       "\n",
+       "beta_outcome (14)\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data\n",
+       "\n",
+       "X_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome\n",
+       "\n",
+       "mu_outcome\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment\n",
+       "\n",
+       "mu_treatment\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "X_data->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_outcome\n",
+       "\n",
+       "likelihood_outcome\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_outcome->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "y_data\n",
+       "\n",
+       "y_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood_treatment\n",
+       "\n",
+       "likelihood_treatment\n",
+       "~\n",
+       "Bernoulli\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data\n",
+       "\n",
+       "t_data\n",
+       "~\n",
+       "Data\n",
+       "\n",
+       "\n",
+       "\n",
+       "t_data->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_treatment->likelihood_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha\n",
+       "\n",
+       "alpha\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "alpha->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr\n",
+       "\n",
+       "rho_unconstr\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho\n",
+       "\n",
+       "rho\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho_unconstr->rho\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps\n",
+       "\n",
+       "eps\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "rho->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U\n",
+       "\n",
+       "sigma_U\n",
+       "~\n",
+       "Halfnormal\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->likelihood_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "sigma_U->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw\n",
+       "\n",
+       "eps_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps_raw->eps\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "eps->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T\n",
+       "\n",
+       "beta_T\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_T->mu_treatment\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O\n",
+       "\n",
+       "beta_O\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_O->mu_outcome\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import causalpy as cp\n",
+    "\n",
+    "df_nhefs = cp.load_data(\"nhefs\")\n",
+    "\n",
+    "features = [\n",
+    "    \"age\",\n",
+    "    \"race\",\n",
+    "    \"sex\",\n",
+    "    \"smokeintensity\",\n",
+    "    \"smokeyrs\",\n",
+    "    \"wt71\",\n",
+    "    \"active_1\",\n",
+    "    \"active_2\",\n",
+    "    \"education_2\",\n",
+    "    \"education_3\",\n",
+    "    \"education_4\",\n",
+    "    \"education_5\",\n",
+    "    \"exercise_1\",\n",
+    "    \"exercise_2\",\n",
+    "]\n",
+    "X = df_nhefs[features]\n",
+    "X = (X - X.mean(axis=0)) / X.std(axis=0)\n",
+    "Y = df_nhefs[\"outcome\"].values\n",
+    "T = df_nhefs[\"trt\"].values\n",
+    "\n",
+    "\n",
+    "coords = {\n",
+    "    \"beta_outcome\": features,\n",
+    "    \"beta_treatment\": features,\n",
+    "    \"obs\": range(df_nhefs.shape[0]),\n",
+    "    \"latent\": [\"U\", \"V\"],\n",
+    "    \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n",
+    "    \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n",
+    "}\n",
+    "\n",
+    "priors = {\n",
+    "    \"rho\": [0.0, 0.5],\n",
+    "    \"alpha\": [0, 3],\n",
+    "    \"beta_O\": [0, 3],\n",
+    "    \"eps\": [0, 1],\n",
+    "    \"sigma_U\": [0.5],\n",
+    "}\n",
+    "\n",
+    "nhefs_binary_model = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=False,\n",
+    ")\n",
+    "pm.model_to_graphviz(nhefs_binary_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model is specified without the observed outcomes deliberately. We feed in the predictor $X$ and now we validate how the model specification can recover accurate treatment effects.\n",
+    "\n",
+    "#### Parameter Recovery\n",
+    "\n",
+    "We \"forward\" sample from the system with known parameters. This generates a synthetic observation data that we will feed back into the model, to condition on data known to have been sampled from this model. This makes use of PyMC's do-syntax. We are intervening on the data generating process to set values of the parameters in the system. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n"
+     ]
+    }
+   ],
+   "source": [
+    "fixed_parameters = {\n",
+    "    \"rho\": 0.6,\n",
+    "    \"alpha\": 3,\n",
+    "    \"beta_O\": [0, 1, 0.4, 0.3, 0.1, 0.8, 0, 0, 0, 0, 0, 0, 3, 0],\n",
+    "    \"beta_T\": [1, 1.3, 0.5, 0.3, 0.7, 1.6, 0, 0.4, 0, 0, 0, 0, 0, 0],\n",
+    "}\n",
+    "with pm.do(nhefs_binary_model, fixed_parameters) as synthetic_model:\n",
+    "    idata = pm.sample_prior_predictive(\n",
+    "        random_seed=1000\n",
+    "    )  # Sample from prior predictive distribution.\n",
+    "    synthetic_y = idata[\"prior\"][\"likelihood_outcome\"].sel(draw=0, chain=0)\n",
+    "    synthetic_t = idata[\"prior\"][\"likelihood_treatment\"].sel(draw=0, chain=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now infer the probable parameters conditioned on the synthetic observed dats. That is, we condition our model on the data generated in our forward pass and attempt the backwards inference. Given the synthetic observations what is the most plausible parameterisation of the world-state that generated the data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57cd792a315b474e86b0cfd36075675d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 500 draw iterations (8_000 + 2_000 draws total) took 12 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Infer parameters conditioned on observed data\n",
+    "with pm.observe(\n",
+    "    nhefs_binary_model,\n",
+    "    {\"likelihood_outcome\": synthetic_y, \"likelihood_treatment\": synthetic_t},\n",
+    ") as inference_model:\n",
+    "    idata_sim = pm.sample_prior_predictive()\n",
+    "    idata_sim.extend(pm.sample(random_seed=100, chains=4, tune=2000, draws=500))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The inferential move allows us to accurately recover the focal parameters. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = az.plot_pair(idata_sim, var_names=[\"alpha\", \"rho\"], kind=\"kde\", figsize=(15, 4))\n",
+    "ax.axhline(0.6, linestyle=\"--\", color=\"red\", label=\"True Rho\")\n",
+    "ax.axvline(3, linestyle=\"--\", color=\"black\", label=\"True Alpha\")\n",
+    "ax.set_title(\"Parameter Recovery with NHEFS data\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter recovery is also extended to the covariate weights in the system. This is promising. It suggests that our model is able to recover true parameters from the data. Recovering covariate weights validates that the model correctly decomposes variation across the entire causal system, not just the focal treatment parameter.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(idata_sim, var_names=[\"beta_O\"], ref_val=fixed_parameters[\"beta_O\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Conditional Update on Observed Data\n",
+    "\n",
+    "Next we will condition on the actual observed data and apply a range of priors to the $\\rho$ term to test the sensitivity of our findings to prior weights. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "760477ae4e7f4709b6d86ec92045c293",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 36 seconds.\n",
+      "Sampling: [alpha, alpha_O_raw, beta_O, eps_raw, gamma_O_u, likelihood_outcome, likelihood_treatment, mu_treatment_bart, pi_O, rho_unconstr, sigma_U]\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "CompoundStep\n",
+      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, pi_O, alpha_O_raw, gamma_O_u, beta_O]\n",
+      ">PGBART: [mu_treatment_bart]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec720fce4df8428ba8ded22ff3e64912",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 183 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b37e364570ad459ebaea2c23551743a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 38 seconds.\n",
+      "Sampling: [alpha, beta_O_raw, beta_T, eps_raw, gamma_O_b_u, likelihood_outcome, likelihood_treatment, pi_O_b, rho_unconstr, sigma_U]\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, pi_O_b, beta_O_raw, gamma_O_b_u]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc8b7d08e5dc4d5f9cf28044654b6be9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
+      "  return 0.5 * np.dot(x, v_out)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot\n",
+      "  return 0.5 * np.dot(x, v_out)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 178 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "sampler_kwargs = {\n",
+    "    \"tune\": 2000,\n",
+    "    \"draws\": 1000,\n",
+    "    \"target_accept\": 0.95,\n",
+    "    \"mp_ctx\": \"spawn\",\n",
+    "    \"random_seed\": 1040,\n",
+    "    # \"cores\": 1\n",
+    "}\n",
+    "priors = {\n",
+    "    \"rho\": [0.0, 0.5],\n",
+    "    \"alpha\": [0, 3],\n",
+    "    \"beta_O\": [0, 3],\n",
+    "    \"eps\": [0, 1],\n",
+    "    \"sigma_U\": [0.5],\n",
+    "}\n",
+    "priors_no_confounding = {\n",
+    "    \"rho\": [0.0, 0.001],\n",
+    "    \"alpha\": [0, 3],\n",
+    "    \"beta_O\": [0, 3],\n",
+    "    \"eps\": [0, 1],\n",
+    "    \"sigma_U\": [0.5],\n",
+    "}\n",
+    "\n",
+    "nhefs_binary_model = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=True,\n",
+    ")\n",
+    "nhefs_binary_model_cate = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=True,\n",
+    "    cate_estimation=True,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=True,\n",
+    ")\n",
+    "nhefs_binary_model_0_rho = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors_no_confounding,\n",
+    "    observed=True,\n",
+    ")\n",
+    "\n",
+    "nhefs_binary_model_s_s = make_binary_model(\n",
+    "    df_nhefs,\n",
+    "    coords,\n",
+    "    bart_treatment=False,\n",
+    "    cate_estimation=False,\n",
+    "    X=X,\n",
+    "    Y=Y,\n",
+    "    T=T,\n",
+    "    priors=priors,\n",
+    "    observed=True,\n",
+    "    spike_and_slab=True,\n",
+    ")\n",
+    "\n",
+    "with nhefs_binary_model:\n",
+    "    idata_nhefs = pm.sample_prior_predictive()\n",
+    "    idata_nhefs.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )\n",
+    "\n",
+    "with nhefs_binary_model_cate:\n",
+    "    idata_nhefs_cate = pm.sample_prior_predictive()\n",
+    "    idata_nhefs_cate.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )\n",
+    "\n",
+    "with nhefs_binary_model_0_rho:\n",
+    "    idata_nhefs_0_rho = pm.sample_prior_predictive()\n",
+    "    idata_nhefs_0_rho.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )\n",
+    "\n",
+    "with nhefs_binary_model_s_s:\n",
+    "    idata_nhefs_s_s = pm.sample_prior_predictive()\n",
+    "    idata_nhefs_s_s.extend(\n",
+    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The predictive comparison shows effectively comparable model performance metrics, with the linear model exhibited the \"best\" metric. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAGbCAYAAACI4ZeUAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAaHBJREFUeJzt3XdYFNf7NvB76aJIUcACdlmjIGChKKJYYi9YkMQuFkBRgRg1MZqo+VoS1ACiBI0FLFjQ2DUWbDGIRoyxJCo2NCJKUUSp+/7hy/5cabvL6sByf67LK9mZM2eeZ1eXh3POzIgkEokERERERCQIDaEDICIiIqrKWIwRERERCYjFGBEREZGAWIwRERERCYjFGBEREZGAWIwRERERCYjFGBEREZGAWIwRERERCYjFGBEREZGAWIwREQkkLi4OYrEYcXFxSh0/e/ZsdO3aVcVRqTexWIyQkBChwyCSwWKMiKqswmJILBYjIiKi2DYbNmyQtvn9998/coREVBWwGCOiKk9XVxd79+4tdt+vv/4KXV3djxwRfSh//vknJk+eLHQYRDJYjBFRlefm5oZ///0XN27ckNl+69YtXL9+nVOBlVx+fj7evHkDAKhevTp0dHQEjohIFosxIqryOnToADMzM/z6668y23fv3o3atWujY8eOxR6XkpKCr7/+Gi4uLrC2tsann36K0NBQ5ObmFmkbFxeHYcOGoXXr1nB1dcWKFSuQl5dXbL9paWn4/vvv0bVrV1hbW6NTp05YsGABXrx4oXSOWVlZ+Omnn9CrVy/Y2NjA2dkZ48ePx8WLF2XanTt3DiNHjoS9vT3s7e0xcuRInD9/vkh/YrEYX3zxBc6dO4chQ4agdevW6NmzJw4dOgQAuHbtGkaNGgU7Ozu4uroiMjKyxD5OnToFd3d32NjYoFu3btiwYUORtrt374aXl5f0ve7SpQsWLlyIzMxMmXaFU8/bt2/H+vXr0aNHD9jY2ODs2bPSc767Ziw/Px8RERHo27cv7Ozs0K5dO/Tv37/IurKcnByEhobi008/hbW1NVxcXPD111/j2bNnMu1iYmIgFotx5swZhIWFoXPnzrCxsYGnpydu3rxZyidEVZmW0AEQEQlNQ0MD/fr1w759+zBz5kxoamqioKAA+/fvR9++faGpqVnkmIyMDAwfPhxPnz6Fp6cnGjdujPPnzyMkJAT//vsvgoODpW3//PNPeHl5oVatWvD29oauri5iYmJw+vTpYvv19PREWloaPDw8YGlpicTERGzduhWXL19GdHS0wiM7b968wejRo3H16lX07NkTI0eORHZ2Ni5fvoz4+Hi0a9cOAHDkyBHMmDEDDRo0gI+PDwBg165dGD9+PEJCQtC9e3eZfv/55x/MnDkTn332GQYOHIjNmzcjICAAIpEICxcuhLu7O3r16oWdO3di0aJFaNGiBdq3by/Tx82bN3Hy5El4enrC3d0dhw8fxuLFi5GTk4NJkyZJ20VGRqJBgwYYPXo0DA0Nce3aNURHR+Off/5BVFRUkZwjIyPx5s0bDB06FDVq1ED9+vWLfW/CwsIQGhqKwYMHY/To0cjNzcW9e/dw4cIFmXYzZszA8ePH0aNHD4wZMwZ3797Ftm3bcOHCBezatQs1a9aUab9y5UoAwLhx45CdnY1ffvkFvr6+OHr0KLS0+KOX3iMhIqqi/vjjD4mVlZVk+/btkn/++UdiZWUlOXXqlEQikUjOnDkjsbKykly7dk2ya9cuiZWVleTcuXPSY5ctWyaxsrKSHDhwQKbPefPmSaysrCSnT5+Wbhs2bJjEzs5O8t9//0m3vXz5UuLm5iaxsrKS/PHHH9Lt3377raRNmzaS+/fvy/R74sQJiZWVlWTr1q3SbbNmzZK4ubmVmeeqVaskVlZWko0bNxbZV1BQIJFIJJLc3FxJp06dJC4uLpL09HTp/rS0NImLi4vE1dVVkpubK91uZWUladGiheTvv/+Wbrt586bEyspKIhaLJbGxsdLtT58+lbRq1UoSEBAgc24rKyuJlZWVTNucnBzJsGHDJK1bt5akpaVJt7969apI7DExMRIrKyvJn3/+Kd1W+Jk6ODjIHP/uOYODg6WvBw4cKJkwYUKRdu+KjY2VWFlZSebNmyez/cCBAxIrKyvJjz/+KN1W+Hdl0KBBkpycHOn23377rUiuRIU4TUlEBMDKygqffPKJdKpyz549aN68OVq2bFls+xMnTqBBgwbo06ePzPbCxeHHjx8HADx//hxXrlxB3759UadOHWm7GjVqwNPTU+ZYiUSCgwcPwsXFBTVq1EBqaqr0j62tLfT19ZW6ovPQoUOoV68eRo4cWWSfSCQC8HZaMTk5GZ6enjA0NJTuNzIywvDhw/HkyRNcv35d5lhbW1u0atVK+losFkNfXx9169ZF586dpdtNTU1hYWGBBw8eFDl/48aNZdpqa2tj1KhRePPmjUyu+vr6AICCggK8fPkSqamp0hG9q1evFul3wIABMDIyKvV9AQADAwPcvn0bt27dKrHNiRMnAADe3t4y23v37g1LS0vpZ/2u4cOHQ1tbW/q6cESwuPeAiGOlRET/36BBg7By5Uo8ffoUx44dg6+vb4ltk5KSil1LVq9ePejr6yMpKQkA8PDhQwBAkyZNirR9f1tqairS09Nx+PBhHD58uNjzpqamyp1Pofv378PJyQkaGiX//l0Yb7NmzYrsK9yWlJSE1q1bS7fXq1evSFsDAwPUrVu3yPaaNWsiPT29yPZGjRoV2da4cWOZmADgr7/+wooVK3Dp0iVkZ2fLtC9uLV2DBg2KbCvOjBkzMGXKFPTr1w8NGzaEo6MjunfvLlMgJiUlSYvMd4lEIjRt2rTYNXXvT4sWFrgZGRlyxUVVC4sxIqL/r1+/fli2bBlmzpyJ7OxsDBgwQOm+CkecJBKJzOvSFBQUAAC6d++OESNGFNvGwMBA6ZhUraTirrg1dsD/vRfvkud9efToEUaPHo369etj5syZqF+/PvT09JCfn48JEyYU26+enl6Z/QJA27ZtcezYMZw+fRp//PEHzp07h+3bt8PNzQ1hYWHSHEuLs7h9irwHRCzGiIj+v8IrJ0+fPg1nZ2eZacX3WVhY4M6dO0W2//fff8jKyoKFhQUAwNLSEgCKbZuYmCjz2sTEBAYGBnjz5g06dOhQnlRkNGzYELdu3UJBQUGJBVRhvLdv30bPnj1l9hXGXthGle7evVvitsLzHTt2DK9fv0Z4eLhMDO+/f8qqUaMG+vTpgz59+kAikWDZsmX45ZdfcOHCBTg5OcHCwgJnz57Ff//9V2R0LDEx8YO8L1S1cM0YEdE7pk6diqlTp8LPz6/Udt26dcODBw+KTCf+/PPP0v3A2wKvdevWOHDgAJ48eSJtl5mZiW3btskcq6mpiT59+uDcuXPFTn3l5+cXO9VXlj59+uDx48fYsmVLkX2FIzWtWrWCubk5oqOjZab9Xrx4gejoaNSpU0dmfZiq3L17F6dOnZK+zs3NRWRkJHR1daUFaeEo0/ujSiU9NUER70/7ikQitGjRAgCk73XhZ/n++Q4dOoQHDx5I9xMpiyNjRETvsLW1ha2tbZntJk6ciIMHD+KLL77ApUuX0KhRI8TFxeHIkSPo2bMnXFxcpG1nzZqFsWPHYvjw4fD09ISOjg5iYmJgaGiIR48eyfQbEBCA+Ph4TJgwAQMGDIC1tTXy8/Px4MEDHD16FH5+fhg2bJhCOY0fPx7Hjh3DwoULcfHiRbRr1w55eXn4888/0bJlS3h7e0NLSwtff/01ZsyYgWHDhmHo0KGQSCTYtWsXUlJSEBISUuLUW3k0b94cAQEB+Oyzz2Bubo5Dhw7hypUr8Pf3ly7A79SpE3R1dTFp0iQMHz4cGhoaOHnypFKF6fv69OmDNm3awMbGBqampnj06BG2bNkCU1NTaTHo6uqKbt26YfPmzXj27BkcHR1x7949bN26FZaWlpgwYUK546CqjcUYEZESDA0NsW3bNqxcuRIHDx5ERkYG6tatCz8/vyKP22nXrh0iIiIQFBSEsLAwGBkZYfDgwXBwcMD48eNl2hoZGWH79u2IiIjAkSNHsH//fujp6aFu3bro16+fUtOXurq62LRpE8LDw3Ho0CEcO3YMBgYGaNmypcx9v3r27ImIiAisXr0aq1atAvB2xGz+/PkqnTZ9V4sWLTBz5kysXLkSt2/fhqmpKWbPno1x48ZJ2zRs2BDh4eFYvnw5Vq5cCT09PXTp0gVBQUFwdnYu1/nHjRuHkydPYsOGDXj16hXMzMzQo0cPeHt7y9w7bOXKlfj555/x66+/4sSJEzA0NMSAAQPg7+9f5B5jRIoSSbiakIiIBCAWi9G/f3/8+OOPQodCJCiuGSMiIiISEIsxIiIiIgGxGCMiIiISENeMEREREQmII2NEREREAmIxRkRERCQgFmNERAqIiYmBWCzG77//Lnfbdx94rYiQkBCIxWLcv39fqeNVqWvXrpg9e7ZK+qpIeRFVBCzGiIhILZw/fx4hISEyj3MqtHPnTmzYsOHjB0UkB96Bn4joAxkwYAB69uwJfX19oUMptwMHDnyQxyGp0h9//IE1a9bA3d29yF3xd+3aheTkZIwdO1aY4IhKwZExIqIPREtLC9WrV4dIJBI6lDJlZmaWur9atWrQ0dH5SNFUHhKJBFlZWUKHQZUcizEiIiXk5+cjLCwMnTt3ho2NDTw9PXHz5k2ZNiWtGTt37hyGDBkCGxsbdOrUCUFBQTh37hzEYjHi4uKKnOvNmzf43//+B2dnZ9ja2mLChAlFHjAOAGlpafj+++/RtWtXWFtbo1OnTliwYEGRabvZs2dDLBbjyZMnCAwMhKOjI9zc3ErNt7g1Yzt27IC7uzvs7e1hb2+PXr16Yd68eaX2866srCzMmzcPTk5OsLOzw+TJk/HgwYMi7V6/fo3g4GD07NkT1tbWcHZ2xpdffoknT57I5LRmzRoAQLdu3SAWi6XvZ9euXfHnn3/i0aNH0u1isVjmHCdPnsTIkSNhb28PW1tbeHp6IjY2VqZNUlISxGIxVqxYgT179qB///6wsbHB9u3b5c6ZqDicpiQiUsLKlSsBvH3QdHZ2Nn755Rf4+vri6NGj0NIq+av1woULmDRpEszNzeHr6wsdHR38+uuvOHv2bInHfPXVVzAwMMCUKVOQkpKCDRs2YObMmdiyZYu0TUZGBjw9PZGWlgYPDw9YWloiMTERW7duxeXLlxEdHV1kZGvixImwtLTE9OnTyxwZe19MTAzmzp2Lbt26YdiwYRCJRHj48CFOnjwpdx+zZ89GjRo14Ovri+TkZERFRWHkyJHYu3cvjIyMAAA5OTkYN24cbty4gaFDh8LKygqPHz/Gli1bcOHCBcTExMDExATDhw/HixcvcPz4ccyZMwfGxsYAgKZNm+Krr77CDz/8gIyMDMyZM6dIHFFRUVi4cCE6duyI6dOnQyQS4cCBA/D29kZQUBD69u0r0/7EiRN49uwZPv/8c5iamqJRo0YKvXdE72MxRkSkhIKCAmzfvh3a2toA3v7QnzJlCs6dO4fOnTuXeNzSpUuhq6uL6OhomJqaAgA+++wzDBw4sMRjTE1NsXr1aul0p7GxMRYvXozbt2+jWbNmAN4Wh8+ePcPu3bvRoEED6bFOTk7w9vZGTEwMPD09Zfq1trbG4sWLlcr/+PHjaNasGcLCwmS2f/nll3L3Ua1aNWzcuFFavLZt2xY+Pj6IiIjAzJkzAQCbNm3C1atXERUVBXt7e+mxvXr1wpAhQ7B+/XoEBgbC3t4ezZs3x/Hjx9G9e3dYWFhI23bv3h3r1q1Dbm5ukfc5OTkZS5YswYgRI2RG9UaOHAlPT08sXboUvXv3hobG/00k3b17FwcOHEDDhg3lzpWoNJymJCJSwvDhw6WFGAC0b98eAIqdZiuUkpKCv//+G/369ZMWYgCgr6+P4cOHl3jciBEjZNadOTg4yJxLIpHg4MGDcHFxQY0aNZCamir9Y2trC319/WJvxTF69Gg5sy2qZs2aePLkCS5duqR0HyNHjpQZRezatSsaNWokM7p24MABtGrVCg0bNpTJy9zcHA0bNpTrFiOlOXLkCHJzc+Hu7i7Tf0ZGBjp37ozk5GTcuXNH5hhXV1cWYqRSHBkjIlJC/fr1ZV4bGhoCeDtdWJLCdV6NGzcusq+0qa73z1V4pWDhuVJTU5Geno7Dhw/j8OHDxfaRmppaZJulpWWJ5yzLpEmTEBcXh88//xx16tSRrjv79NNP5b7qsrj3oXHjxjIFVmJiIt68eQNnZ+di+6hXr55yCbzTPwAMHTq0xDbPnz9H8+bNpa/fHXkkUgUWY0RESiip4Cjtcb/KPgq4rHMVFBQAeDsdN2LEiGLbGhgYFNmmp6enVDzA26Lp0KFDOHv2LM6fP48//vgDv/76K6ytrREVFYVq1aqV2Yc8V5kWFBTA3t4e06ZNK3Z/ea/wLHzvQkNDUb169WLbtGjRQua1rq5uuc5J9D4WY0REH0nhOqa7d+8W2Xfv3j2l+zUxMYGBgQHevHmDDh06KN2PonR1ddGtWzd069YNABAZGYlFixbh4MGDGDJkSJnHJyYmomXLljLb7t69K7Peq1GjRkhPT5crr9KKu5L2FY5Impqaws7OrsxzEH0IXDNGRPSRmJqaolWrVti/fz9SUlKk27Oyssp1ewRNTU306dMH586dw/nz54vsz8/PR3p6utL9F6e4ac9PPvkEAOQ+V1RUFPLy8qSvT5w4gXv37qFLly7Sbf369cPdu3cRExNT5HiJRCITR+HNdYu7A3+1atWK3d6zZ09oa2sjODgYubm5RfY/f/5crlyIyoMjY0REH9GXX34JLy8vDB8+HB4eHtDR0cGePXtQo0YNAPJN3RUnICAA8fHxmDBhAgYMGABra2vk5+fjwYMHOHr0KPz8/DBs2DCV5eHl5QUjIyO0bdsW5ubmeP78OaKjo1GtWjX06NFDrj5ev36NMWPGoFevXkhOTkZkZCRMTU0xYcIEaZtx48bh9OnTmDNnDmJjY9G2bVtoaWkhKSkJJ06cQK9eveDv7w/g7dWhALB8+XL069cP2tracHJyQq1atWBtbY2zZ89i0aJFsLW1hYaGBvr27Yv69etjzpw5WLhwIQYOHIg+ffrA3NwcycnJuHLlCm7fvq3Q7TqIlMFijIjoI3JycsKaNWuwYsUKhIaGwsTEBO7u7rC2tsbUqVOVXsdlZGSE7du3IyIiAkeOHMH+/fuhp6eHunXrol+/fiqfvvzss89w4MABbNmyBS9evICJiQnatGkDHx8fuRe4L168GNu2bUNoaCiys7Ph4OCAuXPnwsTERNpGR0cH69evx4YNG7B//37ExsZCS0sLderUQceOHWXuAdahQwf4+PggJiYGc+bMQUFBATZt2oRatWphwoQJePDgAfbu3YuoqChIJBLpsSNGjECTJk2wbt06bNq0CVlZWahduzZatGiBwMBAlb5vRMURSZRdUUpERCrzyy+/YOnSpThz5gzMzMyEDoeIPiKuGSMi+ojy8/ORk5Mjs+3Vq1fYvHkzmjZtykKMqAriNCUR0UeUkZGBgQMHon///mjYsCFSUlKwZ88ePHr0CCEhIUKHR0QCYDFGRPQR6evrw8nJCYcPH8azZ8+gqamJTz75BN98802pj1EiIvXFNWNEREREAuKaMSIiIiIBsRgjIiIiEhDXjBG9Jy0tTegQVMrQ0LDUh1ers6qcO1A189+wYQMAYPr06VUu93dVxc++UEXL3djYuMw2LMaI1JyGRtUdAK/KuQNVM/9OnToBqJq5v6sq518Zc2cxRkREaqNp06ZCh0CksMpXPhIRERGpERZjRESkNnbu3ImdO3cKHQaRQjhNSUREaiMzM1PoEIgUxpExIiIiIgGxGCMiIiISEIsxIiIiIgGxGCMiIiISEBfwExGR2mjZsqXQIRApjMUYERGpDQcHB6FDIFIYpymJiIiIBMRijIiI1MbJkydx8uRJocMgUginKYmISG3cv39f6BCIFMaRMSIiIiIBsRgjIiIiEhCLMSIiIiIBsRgjIiIiEhAX8BMRkdowNzcXOgQihbEYIyIitdG7d2+hQyBSGKcpiYiIiATEYoyIiNTG1atXcfXqVaHDIFIIizEiIlIbly5dwqVLl4QOg0ghLMaIiIiIBMRijIiIiEhALMaIiIiIBMRijIiIiEhAvM8YERGpDS0t/lijyod/a4mISG2MHDlS6BCIFMZpSiIiIiIBcWSMiIjUxuPHjwEAxsbGAkdCJD8WY0REpDaOHj0KAGjVqpXAkRDJr9JNU8bFxUEsFuP06dMq6zMlJQW+vr5wdHSEWCxGSEiISvqdPXs2OnbsWGa7mJgYiMViJCUlqeS8QkhKSoJYLEZMTIx02+zZs9G1a1cBoyIiIqr4ODIGICQkBHFxcViyZAlMTU1Rp06dj3r+Ll26IDo6GmZmZh/1vB+ar68vRo8eLXQYRFRFPH36FKdPn0ZmZiY0NTXRs2dPtfteJfXEYgzA7du30aJFC/To0UOQ85uYmMDExOSjnzcnJwc6OjofrP8GDRp8sL7LSyKRIDc394PmT0QfR0FBARYsWIDVq1cjNzcXAHDs2DFoa2vDx8cH8+bNg4ZGpZsIoiqkQv3tDAkJgVgsRmJiIry9vWFvbw83NzcEBwcjPz9fpm12djYWLVoER0dHODo6IjAwEOnp6TJt8vPzsXbtWvTp0wfW1tbo0KED5s+fj8zMTAD/N7V26dIlXLx4EWKxWDpdmJ+fj9DQUHz66aewsbGBg4MDhg4diuPHjyuc119//YXhw4ejdevWcHNzw4YNG2T2FzdN2bVrV/j7++PQoUPo06cPbG1tMXjwYFy4cEHm2KtXr2L69Ono0qULWrdujR49emDhwoV4+fKlTLvCKdMrV67gs88+g62tLX744QdMmDAB/fv3LxJzWloabGxssHr1aoXzffec705TFr7fmzdvRkhICFxcXNC2bVtMmjRJuuj2XTt37sSgQYPQunVrODg4IDAwEMnJyTJtDhw4gDFjxqBDhw6ws7ND//79sXHjxiJ/Xwrfz5iYGPTu3RvW1tYqneomIuEsWLAAwcHBaNiwIVavXo3z589j9erVaNiwIYKDg7Fw4UKhQyQqVYUcGfPz84O7uztGjx6NEydOYNWqVahbty6GDRsmbbN48WJ07twZQUFBuHfvHpYtWwYNDQ388MMP0jazZs3C0aNH4eXlhXbt2uHhw4dYuXIlbt26haioKJiZmSE6Ohpz586FtrY25s+fDwAwMzNDREQE1q5dixkzZqBly5bIysrCzZs3ixR8ZcnKysK0adMwfvx4TJ06FQcPHsTixYuhr68PDw+PUo+9fPky7t+/Dz8/P+jp6SE4OBg+Pj44ceIEDA0NAQCPHj2ClZUVBgwYAAMDA9y/fx8RERG4du0atm3bJtPfq1ev4O/vj3HjxmH69OnQ09NDeno6Jk+ejIsXL6Jdu3bStrt27YJEIsHQoUMVylce69atg62tLb7//nukpqZiyZIlmDlzJjZv3ixts2LFCvz8888YMWIEAgMDkZqaip9++gmjR4/G7t27oa+vDwB48OABevToAS8vL2hra+Pq1atYuXIlUlNT4e/vL3Pe+Ph43Lp1C1OmTIGxsTEsLS1VnhsRfVzJyclYvXo1mjVrhvj4eBgZGQEAnJyc4Onpifbt2yMsLAw+Pj6csqQKq0IWY2PGjJEWKh06dMAff/yBQ4cOyRRj9vb20uLJxcUFiYmJ2L59O5YtWwaRSISLFy9i3759WLRokcxxFhYW8PLywqlTp+Dm5gY7Ozvo6+tDV1cXdnZ20naXL19Gx44dMXbsWOm2Ll26KJxLVlYWvv32WwwcOBAA0KlTJ6SkpCAkJASDBw8u9W7RL1++RExMjHQKs3bt2hg6dChOnz4tHc3q1auXzDFt2rRB48aNMWLECNy4cQOffPKJdN/r16/x1VdfoXv37tJtBQUFsLCwwLZt26TFmEQiwfbt29G9e3eYmpoqnHNZzMzMsGLFCunr1NRULFu2DMnJyTA3N8ejR48QERGBSZMmyRRUrVq1Qv/+/RETEyO9saOPj490v0QiQfv27ZGXl4cNGzZgxowZEIlE0v0vXrxATEwMv5ArMHt7e5X2p6GhgYKCApX2WZlUhfxfvHiB3NxcBAYGSguxQkZGRggICICvry+cnZ1Rs2ZNYYIUQFX47EuiitwvX76somjkUyGLsc6dO8u8trKywvXr12W2vV8YWVlZITc3F8+ePYOpqSnOnDkDLS0t9O7dG3l5edJ2Dg4O0NHRQXx8PNzc3EqMwcbGBuHh4QgKCkLnzp1hY2MDXV1dhXMRiURFCqa+ffvizJkzePToERo2bFjisba2tjJrycRiMQDgv//+k27LzMxEeHg4Dh8+jCdPniAnJ0e67+7duzLFmKamZpH3TUNDA5999hl++uknfP311zA2Nsbvv/+O+/fvf7Ch/eI+O+BtXubm5jh37hzy8/MxcOBAmc+uUaNGsLS0RHx8vLQYu3//PkJCQhAfH4+UlBSZ6cnnz5+jdu3a0tetW7eWqxAzNDRUu/UlleWeSx/ifVe3z1JR6p5/4Q/dd3+Zflfh9oKCArV/L95X1fJ9V3lz/9jfmRWyGHv/txsdHR2ZIgOAdJru3TbA27VkwNsfxHl5eWjbtm2x5yhrunHy5MnQ1dXF3r17ERERAT09Pbi5uWHWrFkKXW1pYGBQpIirVasWgLfD66UVY2XlCABfffUVzpw5Az8/P7Rs2RLVq1fHkydPMHXqVLx580bmeCMjo2JH4oYMGYLg4GDExMTAy8sL27ZtQ5MmTeDo6Ch3noqQ57MDgN69exd7fN26dQG8nXYdN24ctLW1MX36dDRo0AC6uro4duwY1qxZUyT/dwuz0mRkZMifTCVgbGyMtLQ0ocOQy6VLl1TaX2XK/UOoCvmHh4djzpw5SEhIgJOTU5H9CQkJAN6uYZ08efJHjk44VeGzL4kqclfleydPYVchizFVMDIygra2NrZs2SIzVVWorDdHW1sbEydOxMSJE5GWlobY2FgsW7YM/v7+2Lp1q9xxvHz5EtnZ2TIFWWGxYW5uLnc/xcnOzsaxY8fg6+uL8ePHS7cXXqDwvuLeB+Dte9G3b19ER0ejX79+OHHiBGbNmlWu2MqjsBj/+eefi73KtHr16gCAK1eu4NGjR4iKikL79u2l+0+cOFFsvyXlT0SV16BBgzBv3jwEBQXB09NT5pf59PR0BAUFQVtbG+7u7sIFSVQGtS3GXF1dERERgbS0tCLTnooyNjaGu7s7EhISsH//foWOlUgkOHz4sHTNGPD2CkBTU1PUr1+/XHHl5uYiPz+/yO0Z3r3xqrxGjBiBmJgYfPnll9DW1sagQYPKFVt5uLi4QFNTE48ePSr1s3v9+jUAyOSfm5uLffv2ffAYiahiMDc3h4+PD4KDg9G+fXsEBATAzs4OCQkJCAoKwp07dzB9+nSuFaUKTW2LMQcHBwwYMAABAQEYM2YMbG1toa2tjcePH+PcuXMYNWoU2rRpU+Lx3t7eEIvFsLa2hqGhIW7duoUDBw6gU6dOCsWhr6+PFStWICMjA40bN8bBgwdx5swZfPfdd6Uu3pdHjRo1YG9vj3Xr1qFWrVowMzPDkSNHcOXKFYX7sra2hq2tLf744w8MGTJE0IWulpaWmDRpEhYvXox79+7ByckJ+vr6SE5ORlxcHFxdXdGrVy/Y29vDwMAA3377Lfz8/JCXl4eNGzdyBIyoipk3bx5EIhHCwsLg6+sr3V64hOGbb74RMDqisqltMQYAS5cuxdatW7Fz506sXbsWmpqaqFevHpydnWFhYVHqsQ4ODjhy5Ai2bduGrKwsmJubw8PDA1OmTFEoBn19ffz0009YtGgRbt68CRMTE8yaNQuenp7lSU0qKCgICxcuxP/+9z/pAv3ly5djyJAhCvfVq1cv6X3IhDZjxgw0a9YMUVFR2LFjByQSCczNzdG+fXvpgn8TExOEhYVhyZIlmDFjBkxMTDBkyBDUqVMHc+fOFTgDIvpYNDQ0MH/+fPj4+GD27NnIzMxE//79eQd+qjREEolEInQQVDGMGjUKWVlZ2LVrl9ChCErdFr1yIW/VzB2omvkX3lTb39+/yuX+rqr42ReqaLlX6QX8JJ+cnBzcvHkTcXFxuHDhgsz9v4iIiOjDYzGmBIlEUuRxO+8r73qwj+Xp06cYNmwYatasiQkTJqBPnz5F2uTn56O0AVQNDY0qfT8bIqo4FF3XS1QRVI6KoYLZvXs35syZU2qbf/755yNFUz4WFhZlxtqjRw88evSoxP1Tp06Fn5+fqkMjIlJY06ZNhQ6BSGEsxpTg5uaGnTt3Ch3GR7N69eoiN919FxfIEhERKY/FmBKMjY0rzeNlVKHwMUxERBVd4QVIEyZMEDgSIvmxGCMiIrXx8uVLoUMgUhhXXRMREREJiMUYERERkYBYjBEREREJiMUYERERkYC4gJ+IiNRGy5YthQ6BSGEsxoiISG04ODgIHQKRwjhNSURERCQgFmNERKQ2YmNjERsbK3QYRArhNCUREamNe/fuCR0CkcI4MkZEREQkIBZjRERERAJiMUZEREQkIBZjRERERALiAn4iIlIbZmZmQodApDAWY0REpDb69OkjdAhECuM0JREREZGAWIwREZHa+Pvvv/H3338LHQaRQliMERGR2rh48SIuXrwodBhECmExRkRERCQgFmNEREREAmIxRkRERCQgFmNEREREAuJ9xoiISG1oamoKHQKRwliMERGR2hg1apTQIRApjNOURERERALiyBgREamN//77DwBgbGwscCRE8mMxRkREauPIkSMAgJYtWwocCZH8OE1JREREJCAWY0REREQCYjFGREREJCAWY0REREQCYjFGREREJCBeTUlERGpj4MCBQodApDAWY0REpDZ4fzGqjFiMERGR2sjJyRE6BCKFcc0YERGpjS1btmDLli1Ch0GkEBZjRERERAJiMUZEREQkIKXWjGVlZSEzMxNmZmbSbc+fP8fWrVuRnp6Ovn37wt7eXmVBEhEREakrpYqx7777Dv/++y92794NAMjOzoanpycePnwIANi2bRsiIyNZkBERERGVQalpyj///BNdunSRvj548CAePnyINWvWIDY2Fo0aNUJ4eLiqYiQiIiJSW0qNjD179gz16tWTvo6NjYWNjY20QBsyZAjWrVunkgCJiIjk5eLiInQIRApTqhjT1taWuZfLhQsXMGTIEOnrGjVq4MWLF+WPjoiISAHNmjUTOgQihSk1TdmkSRMcPXoUEokEx48fR3p6Ojp27Cjd//jxY94FmYiIiEgOSo2MTZgwAX5+fnBwcMDr168hFovh6Ogo3X/+/Hl88sknKguSiIhIHjExMQAALy8vgSMhkp9SxVj37t3xyy+/IDY2FgYGBvj888+hofF2kC0tLQ21a9fGoEGDVBknERFRmbhEhiojpZ9N6ezsDGdn5yLbjY2NERoaWq6giIiIiKoK3oGfiIiISEByjYyNHj1a4Y5FIhE2btyo8HFEREREVYlcxZhEIlG4Y2WOISIiIqpq5CrGIiMjP3QcRERE5cYr+akyUnoBPxERUUXz7m2WiCqLchVj586dw++//47U1FRMmDABTZs2RWZmJq5evYpPPvkERkZGKgqTiIiISD0pdTVlTk4OvL29MWHCBKxbtw579uzB06dPAQA6OjqYMWMGNm/erNJAiYiIynLq1CmcOnVK6DCIFKJUMbZq1SqcOXMGc+fOxaFDh2QW6+vo6KBnz56IjY1VVYxERERyuXv3Lu7evSt0GEQKUaoY279/P4YMGYIRI0YUOxXZpEkTJCUllTc2IiIiIrWnVDGWnJwMGxubEvdXq1YNmZmZSgdFREREVFUoVYwZGxsjOTm5xP23bt2Cubm50kERERERVRVKFWOdOnXCzp078fLlyyL77ty5g127dqFLly7ljY0+gLi4OIjFYpw+fVplfaakpMDX1xeOjo4Qi8UICQlRWd9ERIWePn2K8PBwLFq0COHh4dILx4gqO6VubeHn54fY2FgMHDgQPXv2hEgkwtGjR3HkyBH8+uuvqFGjBry9vVUdK1VQISEhiIuLw5IlS2Bqaoo6deoIHRIRqZGCggIsWLAAq1evRm5urnT7vHnz4OPjg3nz5kFD4+3YgpmZmVBhEilNqWKsbt26iI6OxsKFC7Fx40ZIJBJs3boVIpEIHTp0wLfffovatWurOlaqoG7fvo0WLVqgR48eQodCRGpowYIFCA4ORrNmzRAYGAg7OzskJCQgKCgIwcHBEIlEmD9/PgCgT58+AkdLpDiRpJwPkXz58iXu3r0LiUQCS0tLmJiYqCo2UlBISAhCQ0Nx6NAhLFu2DHFxcTAyMoK7uzumTJkCTU1NxMXFYfTo0QgNDUVcXBz27dsHAHBxccE333wjc3Vsfn4+1q9fj5iYGDx48AA1a9ZEjx49MHPmTNSoUQNJSUno1q1bkTiOHz+OunXrYvXq1di7dy/+++8/VKtWDQ0aNICPj0+xxxTn/v37CAoKwqVLl5CRkQFjY2O0atUKixcvhrGxsVx9HDhwAOvWrZNe6l6nTh0MGDAAPj4+JR6TlpYmV9+VhbGxsdrlJK+qnDugHvknJyejdevWaNiwIeLj42W+o9LT09G+fXvcv38fV69elRkVU4fcy6Mq51/Rcpfn51W5H4dkYGCA1q1bl7cbUiE/Pz+4u7tj9OjROHHiBFatWoW6deti2LBh0jaLFy9G586dERQUhHv37mHZsmXQ0NDADz/8IG0za9YsHD16FF5eXmjXrh0ePnyIlStX4tatW4iKioKZmRmio6Mxd+5caGtrS38zNTMzQ0REBNauXYsZM2agZcuWyMrKws2bN5Geni53HpMmTUKNGjUwd+5c1K5dGykpKfj999+RnZ0t1/GXLl1CYGAgRo4cicDAQIhEIty7dw+PHz+WOwZSHXt7+49+Tg0NDRQUFHz081YU6pD/ixcvkJubi8DAwCK3UjIyMkJAQAB8fX3h7OyMmjVrwsrKCsDbEfvKnnt5qMNnryx5c798+fJHiEY+chVjyv7wqlevnlLHUfmMGTMGHh4eAIAOHTrgjz/+wKFDh2SKMXt7e2nx5OLigsTERGzfvh3Lli2DSCTCxYsXsW/fPixatEjmOAsLC3h5eeHUqVNwc3ODnZ0d9PX1oaurCzs7O2m7y5cvo2PHjhg7dqx0myIXdaSmpuLevXtYtWoVunfvLt2uyBREQkICDAwMMHfuXOm2Dh06lHmcoaGhdP2JupB3JPFDEuo9VbfPUlGVPf/CH6rvfr+8q3B7QUEBNDQ0pIMDt2/frvS5l1dVzl+e3CvC92IhuYqxrl27QiQSKdz5jRs3FD6Gyq9z584yr62srHD9+nWZbe8XRlZWVsjNzcWzZ89gamqKM2fOQEtLC71790ZeXp60nYODA3R0dBAfHw83N7cSY7CxsUF4eDiCgoLQuXNn2NjYQFdXV+4cjI2NYWlpiaCgIDx//hwODg5o3Lix3McXxvDixQsEBARgwIABsLOzk+t5qRkZGQqdp6KrKEP2ly5d+ujnrCi5C0Ud8g8PD8ecOXOQkJAAJyenIvsTEhIAALNnz8bkyZOxYcMGAEBiYmKlz7081OGzV5a8uX+s90dl05T/+9//ZIoxiUSCqKgo3L9/H/369UPTpk0hkUhw584dHDhwAA0bNsTIkSOVj5zK5f2CQ0dHBzk5OTLbDA0Ni7QBIJ0CfP78OfLy8tC2bdtiz1HWdOPkyZOhq6uLvXv3IiIiAnp6enBzc8OsWbPkutpSJBJh/fr1CA0NxYoVK5CWloa6devi888/x8SJE+X65cDBwQHBwcGIjIzE1KlTkZ+fD3t7e3zxxRdo06ZNmccTkfAGDRqEefPmISgoCJ6enkXWjAUFBUFbWxvu7u7CBUlUTnIVY4MHD5Z5vWHDBqSnp+Pw4cMwNTWV2TdlyhR4enoWew8yqjyMjIygra2NLVu2FFv4lFXpa2trY+LEiZg4cSLS0tIQGxuLZcuWwd/fH1u3bpUrBktLSyxduhTA2xsJR0dHIygoCMbGxjJTp6Xp2bMnevbsiezsbFy4cAErVqzAhAkTcPLkySIFKRFVPObm5vDx8UFwcDDat2+PgIAAmasp79y5g+nTp/OWFlSpKbWAf/PmzRg+fHiRQgx4+w9n+PDhiIqKwpgxY8odIAnD1dUVERERSEtLKzLtqShjY2O4u7sjISEB+/fvV6qP5s2bY+7cudixYwf++ecfhY/X1dVFp06d8PLlS/j7++PRo0csxogqiXnz5kEkEiEsLAy+vr7S7dra2pg+fTq++eYbAaMjKj+lirHk5GTptFZxdHR0Sn1cElV8Dg4OGDBgAAICAjBmzBjY2tpCW1sbjx8/xrlz5zBq1KhSp/q8vb0hFothbW0NQ0ND3Lp1CwcOHECnTp3kOv/Nmzfx/fffo1evXmjUqBGAt7epePPmjdx9/PTTT0hJSYGTkxPMzMzw7NkzhIWFoW7dumjWrJlcfRCR8DQ0NDB//nz4+Phg9+7dSElJgampKdzd3YuMiGlqagoUJZHylCrGGjZsiF27dmH48OHQ19eX2ffq1Svs2rULDRs2VEmAJJylS5di69at2LlzJ9auXQtNTU3Uq1cPzs7OsLCwKPVYBwcHHDlyBNu2bUNWVhbMzc3h4eGBKVOmyHVuU1NT1KtXD5s2bUJycjK0tLTQtGlTrFy5Uu6ROltbW0RGRmLJkiVIT0+HsbExHBwcMG3atFJ/mSCiisnMzAyTJ08utc2oUaM+UjREqqPUTV8PHz4Mf39/1K1bF56enmjcuDFEIhHu3LmD6Oho/Pfff1ixYgV69er1IWIm+qDU7QokXlVVNXMHqnb+VTl3oGrnX9Fy/2A3fe3VqxckEgkWL16M5cuXSxd4SyQSmJmZISgoiIUYERF9dP/99x+AinUPKaKyKH0H/t69e6Nnz574+++/kZSUJH0ckrW1dZW+0RyVTSKRID8/v9Q2Wlql/9XMz89HaYO6Ghoa/HtIVAUdOXIEANCyZUuBIyGSX7keh1R4t2M+DokUsXv3bsyZM6fUNmVdMTl27FhcuHChxP3u7u5YsmSJUvERERF9TOUqxu7evYsTJ04gKSkJIpEIFhYWcHNzU/hO6VS1uLm5YefOneXq47vvvsOrV69K3M8pCiIiqiyULsaWLl2KjRs3FnkY548//oixY8fiyy+/LHdwpJ6MjY3LXSw1adJERdEQEREJS+mbvq5fvx6dO3fGxIkT0bRpUwDAnTt3sHbtWqxfvx4WFhb4/PPPVRosERERkbpRqhjbtm0bnJycEB4eLrO9Xbt2aNu2LcaNG4etW7eyGCMiIiIqg1KXm92/fx89evQodp9IJEL37t1x//79cgVGRESkqIEDB2LgwIFCh0GkEKVGxvT19ZGSklLi/pSUlCJ35iciIvrQePEOVUZKjYw5OjoiKioKf/31V5F9V69eRVRUFJycnModHBERkSJyc3ORm5srdBhEClFqZCwgIAB//PEHPD094ejoKLOAPy4uDgYGBvD391dpoERERGXZvHkzAPBnEFUqSj8ofOfOnQgKCsKpU6dw/vx5AEC1atXQq1cv+Pv7w9LSUqWBEhEREakjpe8zZmlpiZUrV6KgoACpqakAABMTEz6ChoiIiEgB5boDP/D2kUi1a9dWRSxEREREVU65irEHDx7g/v37yMjIKPahzf379y9P90RERERqT6li7MmTJ5g9ezbi4uIAoNhCTCQSsRgjIiIiKoNSxdjXX3+N+Ph4jBgxAm3atIGBgYGq4yIiIlJYx44dhQ6BSGFKFWMXL17E2LFjMXPmTFXHQ0REpLTmzZsLHQKRwpS69NHY2Bj16tVTdSxEREREVY5SxdjgwYNx+PDhYteKERERCSUmJgYxMTFCh0GkEKWmKadOnYoFCxZgxIgR8PDwQJ06daCpqVmkXfv27csdIBERkbxevHghdAhEClOqGMvKykJqaiouX76My5cvF9kvkUggEolw48aNcgdIREREpM6UKsbmzp2L3377DT169ICdnR2vpiQiIiJSklLF2JkzZ+Dp6Yn58+erOh4iIiKiKkWpBfy6urq8fJiIiIhIBZQqxnr16oVTp06pOhYiIqJyadGiBVq0aCF0GEQKUWqa0sPDA1999RWmTZsGT09P1KtXr9irKS0tLcsdIBERkbycnJyEDoFIYUoVY4MGDYJIJML169fx22+/ldiOV1MSERERlU6pYmzKlCkQiUSqjoWIiKhcTp8+DQAYOHCgwJEQyU+pYszPz0/VcRAREZVbYmKi0CEQKUypBfxEREREpBosxoiIiIgExGKMiIiISEAsxoiIiIgEpNQCfiIioorI1NRU6BCIFMZijIiI1Ebfvn2FDoFIYZymJCIiIhKQXCNjo0ePVrhjkUiEjRs3KnwcERGRsq5duwYAcHFxETgSIvnJVYxJJJIi25KTk/HgwQPUqFEDlpaWkEgkSEpKQmZmJho2bAhzc3OVB0tERFSa+Ph4ACzGqHKRqxiLjIyUeX316lV4eXnhm2++gYeHB7S1tQEAubm52LZtG0JDQxEUFKT6aImIiIjUjFJrxpYtW4Y+ffpgxIgR0kIMALS1tTFq1Cj06tULS5cuVVmQREREROpKqWLs6tWrEIvFJe5v0aIF/v77b6WDIiIiIqoqlCrGqlevjosXL5a4Pz4+Hvr6+koHRURERFRVKFWM9e3bFwcPHsSSJUvw9OlT6fanT59i8eLFOHToEO/1QkREH52GhgY0NHjXJqpcRJLiLpUsw5s3bzBjxgzExsZCJBJBX18fIpEIr169gkQigaurK0JCQqCrq/shYib6oNLS0oQOQaWMjY3VLid5VeXcgaqdf1XOHaja+Ve03I2Njctso9Qd+PX09LBmzRqcPn0ax48fx6NHjyCRSGBpaYmuXbvC1dVVmW6JiIiIqpxyPQ7J1dWVhRcREVUYT548ASDfaARRRVHuZ1PeuXMHz58/R4sWLVCzZk1VxERERKSUw4cPAwA++eQTgSMhkp/SqxwPHjyILl26oF+/fhgzZoz0ERSpqano2rUrDh48qLIgiYiIiNSVUsXYqVOnEBgYCDMzM0ydOlXmcUkmJiZo1qwZ9u/fr7IgiYiIiNSVUsXY6tWrYWtri+joaHz++edF9tva2uLmzZvlDo6IiIhI3SlVjN28eRN9+/aFSCQqdr+ZmRmePXtWrsCIiIiIqgKlijENDQ2Udnuyp0+folq1akoHRURERFRVKFWMtWrVCqdOnSp2X15eHg4ePAhbW9tyBUZERKSoAQMGYMCAAUKHQaQQpYqx8ePH49y5c1i0aBEeP34MAMjKysJff/2FyZMn4+7duxg/frxKAyUiIiqLiYkJTExMhA6DSCFKPQ4JAKKiorB06VLk5eVBIpFI149pampizpw5GDFihEoDJfpYKtJjNFShoj0a5GOqyrkDVTP/3NxcAG/XLle13N9VFT/7QhUt9w/2OCQAGDlyJHr06IEjR47g7t27KCgoQKNGjdCzZ0/Uq1dP2W6JiIiUtnnzZgCAv7+/wJEQyU+pYuz06dNwdXWFubk5Ro8eXWyb0NBQTJ06tVzBEREREak7pdaMTZ8+HZcvXy5x/8qVK7Fq1SqlgyIiIiKqKpQqxtq3b4/Jkyfj33//LbIvKCgIa9aswaRJk8odHBEREZG6U6oYCwkJQfPmzTF+/Hg8fPhQun3p0qWIiIiAj48P5+uJiIiI5KBUMaarq4vw8HDUrl0b48ePx9OnT7F48WKsX78eU6dOxfTp01UdJxEREZFaUvpqyho1amDt2rX4/PPP0a9fP7x8+RLTpk2Dr6+vKuMjIiKSW8eOHYUOgUhhco2MPXz4sNg/r1+/xnfffYf8/HwMGzYM/fv3l9mvLuLi4iAWi3H69GmV9ZmSkgJfX184OjpCLBYjJCREJf3Onj37o30Z7dixAzExMR/lXCX5/fffVfbeEZH8nj59ivDwcCxatAjh4eF4+vSp0CEBAJo3b47mzZsLHQaRQuQaGevRo0eJDwUHAIlEgh07dmDHjh0y22/cuFG+6NRYSEgI4uLisGTJEpiamqJOnTpCh6SwHTt2QFdXF4MHDxYsht9//x0RERHw8/MTLAaiqqSgoAALFizA6tWrpTdYBYB58+bBx8cH8+bNg4aGUitgiKosuYqxKVOmlFqMkeJu376NFi1aoEePHkKHorCcnBzo6OgIHQYRCWDBggUIDg5Gs2bNEBgYCDs7OyQkJCAoKAjBwcEQiUSYP3++YPHt3r0bAPhIPqpU5CrG1HnUISQkBKGhoTh06BCWLVuGuLg4GBkZwd3dHVOmTIGmpqa0bXZ2NhYtWoR9+/YBAFxcXPDNN9/AyMhI2iY/Px/r169HTEwMHjx4gJo1a6JHjx6YOXMmatSogaSkJHTr1k3aXiwWAwCOHz+OunXrYvXq1di7dy/+++8/VKtWDQ0aNICPj4/MMfL466+/8P333+PGjRuoVasWxowZg7Fjx0r3p6amYuXKlYiLi0NycjIMDAxgY2ODmTNnonHjxtJ2MTExmDNnDjZt2oStW7fi7NmzsLCwgIGBAa5cuSKTg4ODAyIjI+WKLy0tDaGhoTh+/DiePXsGIyMjtG/fHgsWLICBgYFc8YWEhCAiIkImBgD4559/AABv3rxBWFgYDhw4gOTkZNSqVQsDBgyAn58fi0kiJSQnJ2P16tVo1qwZ4uPjpd99Tk5O8PT0RPv27REWFgYfHx+YmZkJEmNGRoYg5yUqD6UX8KsbPz8/uLu7Y/To0Thx4gRWrVqFunXrYtiwYdI2ixcvRufOnREUFIR79+5h2bJl0NDQwA8//CBtM2vWLBw9ehReXl5o164dHj58iJUrV+LWrVuIioqCmZkZoqOjMXfuXGhra0t/gzQzM0NERATWrl2LGTNmoGXLlsjKysLNmzeRnp6uUC5ZWVmYNm0axo8fj6lTp+LgwYNYvHgx9PX14eHhAQBIT0+Hjo4O/P39UatWLaSmpmLLli0YPnw4Dh06hFq1asn0+eWXX6Jv374IDg5Gbm4u6tevjxkzZsjkUKNGDbnie/nyJTw9PZGWlgZvb2988sknSEtLw6lTp5CVlQUDAwO54hs2bBgePnyIX3/9FdHR0TLnyMvLw8SJE3Hz5k14e3ujZcuWuHbtGkJDQ/H48WMEBQUp9J6qC3t7e6FD+Kg0NDRQUFAgdBiCUXX+L168QG5uLgIDA2V+CQUAIyMjBAQEwNfXF87OzqhZs6bKzquIoUOHAgCaNGnCz76S5l/aTeXVVbmKsTt37uDhw4clFguDBg0qT/cf1ZgxY6SFSocOHfDHH3/g0KFDMsWYvb29tPBwcXFBYmIitm/fjmXLlkEkEuHixYvYt28fFi1aJHOchYUFvLy8cOrUKbi5ucHOzg76+vrQ1dWFnZ2dtN3ly5fRsWNHmRGsLl26KJxLVlYWvv32WwwcOBAA0KlTJ6SkpCAkJASDBw+GlpYWmjRpgrlz50qPyc/Ph6urKzp06IB9+/bJxAAAn376Kb788kuZbcXlII+NGzfi/v372L59O1q3bi3d3qdPH+n/yxNfnTp1pL99vx/DgQMHcOHCBaxfvx4dOnQAADg7O8PAwADz5s3D5MmTYWVlVWx8hoaGarfmpfBBteqWlzyqYs7vUmX+hT/cS/o3X7i9oKCgQrzvFSEGIVXW/OV5sPbH6ONjUqoYe/z4Mb788ktcunQJEomk2DYikahSFWOdO3eWeW1lZYXr16/LbHu/MLKyskJubi6ePXsGU1NTnDlzBlpaWujduzfy8vKk7RwcHKCjo4P4+Hi4ubmVGIONjQ3Cw8MRFBSEzp07w8bGBrq6ugrnIhKJ0KtXL5ltffv2xZkzZ/Do0SM0bNgQALBt2zZs27YNDx48wKtXr6Rt7969W6RPRadJS3P27Fm0bNlSphArjiLxve/06dMwMzODg4ODzGfRqVMnAEB8fHyJxZi6TXMYGxsjLS0NAHDp0iWBo/m43s29KlJ1/uHh4ZgzZw4SEhLg5ORUZH9CQgKAt1d1T548WWXnVcSGDRsAAImJifzsK2n+5Y27ouUuT2GoVDE2f/58/P3335g9ezYcHBwEG45WpfeH3HV0dJCTkyOzzdDQsEgb4O1aMgB4/vw58vLy0LZt22LPUdZ04+TJk6Grq4u9e/ciIiICenp6cHNzw6xZsxS62tLAwKBIEVc47ZicnIyGDRsiKioKCxcuxKhRoxAQEAAjIyOIRCJMmjRJms+7TE1N5T5/WVJTU9G0adNS2ygaX3HnePr0KVq1alXsfkWnfono7WzHvHnzEBQUBE9PT5nvzfT0dAQFBUFbWxvu7u7CBUlUCSlVjMXHx2PcuHEYM2aMquOp1IyMjKCtrY0tW7YUe/VpWdWxtrY2Jk6ciIkTJyItLQ2xsbFYtmwZ/P39sXXrVrnjePnyJbKzs2UKsufPnwMAzM3NAbydxnN0dJSZCszNzf0oo0LGxsZITk4utU154zMyMkLdunVLvAeZUIuLiSozc3Nz+Pj4IDg4GO3bt0dAQIDM1ZR37tzB9OnTBf331aJFC8HOTaQspYoxfX191K5dW9WxVHqurq6IiIhAWlpakWlPRRkbG8Pd3R0JCQnYv3+/QsdKJBIcPnxYumYMeFvcmJqaon79+gDeXmlYvXp1meP27NmD/Px8uc+jo6ODN2/eKBQb8HaqMDQ0FFevXoWNjU2xbeSNr3B08s2bN9DT05Nud3V1xZEjR6Crq1vidCQRKW7evHkQiUQICwuTeeKKtrY2pk+fjm+++UbA6FDs9ClRRadUMdavXz8cP34cI0aMUHU8lZqDgwMGDBiAgIAAjBkzBra2ttDW1sbjx49x7tw5jBo1Cm3atCnxeG9vb4jFYlhbW8PQ0BC3bt3CgQMHpOuc5KWvr48VK1YgIyMDjRs3xsGDB3HmzBl899130NJ6+5G7uroiPDwcq1atQps2bfDXX39h8+bNCk05N23aFDExMTh06BAsLCxQvXp1NGnSpMzjxowZg3379sHLyws+Pj5o0aIFXrx4gdjYWMyYMQPm5uZyx9esWTMAwNq1a9GpUydoaGjAxsYG/fv3R0xMDMaNG4exY8eiZcuWKCgoQFJSEk6dOoVvvvlGWpgSkfw0NDQwf/58+Pj4YPfu3UhJSYGpqSnc3d054kykJKWKsaFDh+Krr77ClClT8Pnnn6N+/foy9+MqZGlpWe4AK5ulS5di69at2LlzJ9auXQtNTU3Uq1cPzs7OsLCwKPVYBwcHHDlyBNu2bUNWVhbMzc3h4eGBKVOmKBSDvr4+fvrpJyxatAg3b96EiYkJZs2aBU9PT2kbX19fZGZmYsuWLYiIiICNjQ1+/vlnhZ4t6uPjg6SkJHz11VfIysqS+z5jBgYG2Lp1K4KDg/HLL78gLS0NxsbGcHBwkI6GyRvfp59+Cg8PD0RFRSE0NBQSiQT//PMPtLS0sG7dOqxduxa7d+9GcHAw9PT0UL9+fXTq1KnIGkEiUoyZmZlgi/RLU/jYundnBogqOpGkpMshS9GiRQuIRCJIJJJS78zPxyFRZVSRrsJRhYp2ZdHHVJVzB6pm/oVXU/r7+1e53N9VFT/7QhUt9w92NSUfj0RERESkGkoVY+r8eKSKSiKRlLm4vnA9mJDevadXcTQ1NVnIExERvUP4n94kl927d2POnDmltil8JqOQSrqvV6HFixdj8ODBHykaIiKiiq9cxVheXh4SExPx4sWLYu/E3759+/J0T+9wc3PDzp07hQ6jTGXFWNZFDERERFWN0sVYSEgINmzYgKysrBLbcAG/6hgbG1eKZ22VdN8wIqKPgffApMpIqWJs48aNWLVqFdzd3eHk5IRZs2bhiy++QI0aNRAZGQk9PT188cUXqo6ViIioVP369RM6BCKFKfVI9+3bt8PNzQ2LFy+Gq6srgLdrhTw9PbFr1y68evVK+sBYIiIiIiqZUsXYw4cP4eLiAgDSm73m5uYCAPT09ODu7o5du3apKEQiIiL5XLt2DdeuXRM6DCKFKFWMVatWDQUFBQCA6tWrQ1NTE8+ePZPuNzIyQkpKimoiJCIiklN8fDzi4+OFDoNIIUoVYw0bNkRiYiKAtyNjVlZWOHr0KIC398P67bffULduXdVFSURERKSmlCrGOnXqhMOHD0unJseOHYvY2Fj06NEDn376Kc6ePYtRo0apNFAiIiIidaTU1ZTe3t4YOXKk9I7vAwcOhI6ODg4cOAANDQ34+flhwIABKg2UiIiISB0pXIzl5ubizp07MDIyknmsTe/evdG7d2+VBkdERESk7pSaphwyZAgOHz6s6liIiIjKRSQS8fm3VOkoPDKmra0NU1PTDxELERFRuYwZM0boEIgUptTI2MCBA7F3717pAn4iIiIiUo5SC/jbtGmDEydOYPDgwfDw8IClpSV0dXWLtHN2di53gERERPJKTk4GgErxLF+iQkoVY5MnT5b+//fff19kfl4ikUAkEvFB4URE9FEdOnQIANCiRQuBIyGSn1LF2OLFi1UdBxEREVGVpFQx5u7uruo4iIiIiKokpRbwExEREZFqsBgjIiIiEhCLMSIiIiIBKbVmjIiIqCLq37+/0CEQKYzFGBERqY1atWoJHQKRwliMERGR2sjLyxM6BCKFcc0YERGpjaioKERFRQkdBpFCWIwRERERCYjFGBEREZGAWIwRERERCYjFGBEREZGAWIwRERERCYi3tiAiIrXRoUMHoUMgUhiLMSIiUhtWVlZCh0CkME5TEhEREQmIxRgREamNPXv2YM+ePUKHQaQQTlMSEZHaSE9PFzoEIoVxZIyIiIhIQCzGiIiIiATEYoyIiIhIQCzGiIiIiATEBfxERKQ2xGKx0CEQKYzFGBERqQ1nZ2ehQyBSGKcpiYiIiATEYoyIiNTGmTNncObMGaHDIFIIpymJiEht3LlzR+gQiBTGkTEiIiIiAbEYIyIiIhIQizEiIiIiAbEYIyIiIhIQF/ATEZHaqFWrltAhECmMxRgREamN/v37Cx0CkcI4TUlEREQkIBZjRESkNq5fv47r168LHQaRQliMERGR2rhw4QIuXLggdBhECmExRkRERCQgFmNEREREAmIxRkRERCQgFmNEREREAmIxRkRERCQg3vSViIjUxtixY4UOgUhhHBkjIiIiEhBHxoiISG0kJycDAIyNjQWOhEh+HBmrBOLi4iAWi3H69GmV9ZmSkgJfX184OjpCLBYjJCREZX0XJykpCWKxGFu3bv2g53lfQkICPv/8c9ja2sLZ2Rnz5s3Dy5cvP2oMRIp4+vQpwsPDsWjRIoSHh+Pp06dCh1SpHDp0CIcOHRI6DCKFcGSsigoJCUFcXByWLFkCU1NT1KlTR+iQVO7WrVsYO3YsHBwcEBYWhpSUFPzwww+4e/cuNm3aBJFIJHSIRFIFBQVYsGABVq9ejdzcXOn2efPmwcfHB/PmzYOGBn9/JlJHLMaqqNu3b6NFixbo0aPHBz9XTk7OBz9HcYKDg1GrVi2EhoZCR0cHwNupi0mTJiE2NhZubm6CxEVUnAULFiA4OBjNmjVDYGAg7OzskJCQgKCgIAQHB0MkEmH+/PlCh0lEHwB/zRJYSEgIxGIxEhMT4e3tDXt7e7i5uSE4OBj5+fkybbOzs7Fo0SI4OjrC0dERgYGBSE9Pl2mTn5+PtWvXok+fPrC2tkaHDh0wf/58ZGZmAvi/6cJLly7h4sWLEIvFEIvFSEpKQn5+PkJDQ/Hpp5/CxsYGDg4OGDp0KI4fPy53PrNnz0bHjh1x5coVfPbZZ7C1tcUPP/wg3V9QUICQkBC4uLigbdu2mDRpEh4/fizTR15eHkJDQ9G9e3dYW1ujc+fOWLJkCV6/fi13HLm5uTh16hR69eolLcQAwNXVFSYmJjh27JjcfRF9aMnJyVi9ejWaNWuG+Ph4eHt7w8nJCd7e3oiPj0ezZs0QFhbGKUsiNcWRsQrCz88P7u7uGD16NE6cOIFVq1ahbt26GDZsmLTN4sWL0blzZwQFBeHevXtYtmwZNDQ0ZIqdWbNm4ejRo/Dy8kK7du3w8OFDrFy5Erdu3UJUVBTMzMwQHR2NuXPnQltbW/qbtpmZGSIiIrB27VrMmDEDLVu2RFZWFm7evFmk4CvLq1ev4O/vj3HjxmH69OnQ09OT7lu3bh1sbW3x/fffIzU1FUuWLMHMmTOxefNmaZu5c+di7969mDhxIhwcHHDt2jWEhobi33//xbp16+SaXnzw4AGys7PRvHlzme0ikQjNmjXD7du3FcpJGfb29h/8HPLQ0NBAQUGB0GEIorLk/uLFC+Tm5iIwMBBGRkYy+4yMjBAQEABfX184OzujZs2acvdbWfJXpaFDhwIAmjRpUuVyL3T58mWhQyAFsRirIMaMGQMPDw8AQIcOHfDHH3/g0KFDMsWYvb29tHhycXFBYmIitm/fjmXLlkEkEuHixYvYt28fFi1aJHOchYUFvLy8cOrUKbi5ucHOzg76+vrQ1dWFnZ2dtN3ly5fRsWNHmfv0dOnSReFcXr9+ja+++grdu3eXbktKSgLwtuhbsWKFdHtqaiqWLVuG5ORkmJub4/bt29i9ezd8fX0xffp0AEDHjh1Rs2ZNzJ8/H2fPnkWnTp3KjCEjIwMAYGhoWGSfkZER/v333xKPNTQ0VMnanIq0vqcixfKxVYbcC4uGd/89vqtwe0FBgcL5VIb8P5SqmnvhlaRV+YrSypY7i7EKonPnzjKvrayscP36dZlt7xdGVlZWyM3NxbNnz2BqaoozZ85AS0sLvXv3Rl5enrSdg4MDdHR0EB8fX+o6KRsbG4SHhyMoKAidO3eGjY0NdHV1Fc5FU1OzxCKuuBwA4L///oO5uTkuXLgAAOjfv79Mu4EDB2L+/PmIi4uTqxiTSCQAoNQi/cJCrrwuXbqkkn7Ky9jYGGlpaUKHIYjKknt4eDjmzJmDhIQEODk5FdmfkJAA4O0ygMmTJ8vdb2XJX5WeP38OAPjxxx+rXO6F0tLSquRnX6ii5S5PYchirIJ4f2pCR0enyML390d5CtdCZWdnA3j7JZSXl4e2bdsWe46yphsnT54MXV1d7N27FxEREdDT04ObmxtmzZql0NWWRkZG0NIq/q9WWTkUxmhqairTrlq1ajAwMJB7yrTw/SyufXp6epH3m0hIgwYNwrx58xAUFARPT0+Zv5/p6ekICgqCtrY23N3dhQuykqhVq5bQIRApjMWYGjEyMoK2tja2bNlS7IhQWdW5trY2Jk6ciIkTJyItLQ2xsbFYtmwZ/P39Fbo/WHluGVH4QyglJQUGBgbS7W/evMHLly/lLqIaNGgAXV3dYteG3blzh1dSUoVibm4OHx8fBAcHo3379ggICJC5mvLOnTuYPn06zMzMhA61wnt3VoCosmAxpkZcXV0RERGBtLS0ItOeijI2Noa7uzsSEhKwf/9+FUVYNkdHRwDAvn37pGvGAGDv3r0y+8uira0NV1dXHD58GNOmTZOOwJ0+fRrPnz9Ht27dVBw5UfnMmzcPIpEIYWFh8PX1lW7X1tbG9OnT8c033wgYXeURFRUFAPD39xc4EiL5sRhTIw4ODhgwYAACAgIwZswY2NraQltbG48fP8a5c+cwatQotGnTpsTjvb29IRaLYW1tDUNDQ9y6dQsHDhyQa42WqjRt2hSDBw9GeHg4CgoK4ODggBs3biA4OBgdO3aEi4uL3H35+fnBw8MD06ZNw+jRo6U3fW3Xrh1HxqjC0dDQwPz58+Hj44Pdu3cjJSUFpqamcHd354gYkZpjMaZmli5diq1bt2Lnzp1Yu3YtNDU1Ua9ePTg7O8PCwqLUYx0cHHDkyBFs27YNWVlZMDc3h4eHB6ZMmfKRon9r4cKFqF+/Pnbv3o1169bBxMQEn332GWbMmKHQFKhYLMYvv/yCH3/8Ed7e3qhWrRp69OiBL7/8knffpwrLzMxMoUX6RFT5iSSFl50REQBUqKtwVKGiXVn0MVXl3IGqmf+GDRsAvJ2mrGq5v6sqfvaFKlru8lxNWTVvwkJERERUQXCakuQikUiKPJ7pfSXdzkLVCgoKSr2ztkgkgqam5keJhYiIqLxYjJFcdu/ejTlz5pTa5p9//vkosXz11VfYvXt3ifsdHBwQGRn5UWIhooqlQ4cOQodApDAWYyQXNzc37Ny5U+gwAABTp07FiBEjStxfvXr1jxgNEVUkhU/1IKpMWIyRXIyNjSvMs74sLCzKvDKUiIiosuACfiIiUht79uzBnj17hA6DSCEcGSMiIrUh7/NriSoSjowRERERCYjFGBEREZGAWIwRERERCYjFGBEREZGAuICfiIjUBu8zRpURizEiIlIbvAM/VUacpiQiIiISEIsxIiJSG2fPnsXZs2eFDoNIIZymJCIitXH79m2hQyBSGEfGiIiIiATEYoyIiIhIQCzGiIiIiATEYoyIiIhIQFzAT0REaqNWrVpCh0CkMBZjRESkNvr37y90CEQK4zQlERERkYBYjBERkdq4ceMGbty4IXQYRAphMUZERGojLi4OcXFxQodBpBAWY0REREQCYjFGREREJCAWY0REREQCYjFGREREJCAWY0REREQCEkkkEonQQRARERFVVRwZIyIiIhIQizEiIiIiAbEYIyIiIhIQizEiIiIiAbEYIyIiIhKQltABEJH8Zs+ejd27dxfZ3rNnTwQHB0tfHzt2DBs2bEBiYiJevHgBExMTtGnTBlOnTkWzZs1kjn3w4AF++uknxMfHIyMjA/Xq1cOgQYMwfvx4aGtry7TdtWsX1q1bh4cPH8LMzAyfffYZvLy8IBKJPkzC7/kQ+QPA1atXsWrVKly6dAnZ2dmoX78+Ro0ahc8//1ymnZD5f6jcC4WGhiIkJAS2trbYvn17kf3q9tmfP38ee/bsweXLl5GcnIxatWqhffv2mDZtGurXr1/kPOr22efk5CAkJAR79uxBeno6mjdvDj8/P7i5uRU5T2X57Hfs2IHjx4/jxo0bSE9PR7169dCzZ09MnDgR1atXlzm2on3vsRgjqmSMjIwQHh5eZNu7MjIyYGdnh9GjR8PIyAiPHz/Gzz//DA8PD+zduxcWFhYAgJcvX2LMmDHQ0tJCYGAgzM3NER8fj5UrVyIlJQVz586V9rlr1y589dVXGD9+PLp06YKEhASsWLECWVlZmDZt2gfP+91cVZU/AJw+fRq+vr7o1asXfvjhB+jq6uLu3bvIy8uT6bMi5K/q3AslJiYiIiICtWrVKva8FSF3QLX5b926FS9fvsSECRPQqFEj/PfffwgLC8PgwYOxe/du1KtXT9pnRchf1Z/9N998g6NHj2LmzJlo3Lgxdu/eDV9fX/zyyy9wdnaWtqsIuQPy5R8SEgJnZ2fMmjULtWrVwvXr1xEaGorz589jy5Yt0NTUBFBBv/ckRFRpzJo1S9KhQweljr19+7bEyspKEh4eLt127NgxiZWVleT333+XaTtz5kxJ+/btpa9zc3Mlzs7OkoCAAJl2y5Ytk1hbW0ueP3+uVEyKUnX+mZmZEmdnZ8mCBQtKPbYi5K/q3AsVFBRIRo4cKZk3b55k5MiRkmHDhsnsrwi5SySqz7+4uJOSkiRisViyfPly6baKkL+qc79x44bEyspKEh0dLd1WUFAgGTZsmMTd3V26rSLkLpHIn39x8ezevVtiZWUlOXv2rHRbRfze45oxoirCxMQEAGSG4HNzcwEABgYGMm0NDAyQn58vfZ2QkIDnz59jwIABMu0GDRqEnJwcnDlz5kOFrTLF5X/48GE8f/4cXl5epR5b2fMvLvdCO3fuRGJiIgIDA4s9trLnDhSff+G2d9WvXx/GxsZ48uSJdFtlz7+43E+cOAFNTU306dNHuk0kEmHgwIG4du2aNP/Klntxn6m1tTUAyHymFfF7j8UYUSWTnp4OJycnfPLJJ+jevTtCQkKQk5NTbNv8/Hzk5OTg3r17mD9/PkxNTdG/f3/pfldXV1hYWODHH39EYmIiMjMzcerUKezZswfjxo2Ttrt16xYAwMrKSqb/Jk2aQEtLC7dv3/4AmRZPlflfvHgRRkZGuH37NgYMGICWLVvCxcUFS5culemzouSvytwB4NmzZ/jhhx8wa9Ys1KxZs9h+KkrugOrzf9/t27eRmpoqs76qouSvytxv3bqFevXqoUaNGjLHNW/eXLr/3f8KnTugWP7viouLA/B/uQEV83uPa8aIKpEWLVrgk08+gZWVFXJzc3H69GmsXr0a165dw5o1a4q079u3L+7evQsAaNSoETZu3IjatWtL9+vr62Pr1q2YNm0aevfuLd0+adIkTJ06Vfo6IyMDAIr8wNbU1ESNGjWk+z80Vef/9OlTvH79Gv7+/vD29kbr1q1x6dIlhIWF4cmTJ1ixYgWAipG/qnMHgEWLFqFly5ZFfvN/V0XIHfgw+b8rNzcXc+fOhYmJCYYNGybdXhHyV3XuGRkZxRbfhoaG0v3v/reyffaFHj9+jODgYDg5OaF169bS7RXxe4/FGJFAfv/9d5nfwkpSv359nDhxAgAwduxYmX2urq4wNTXF8uXLcfHiRbRr105mf0hICLKysvDo0SNs2rQJY8aMwcaNG9G0aVMAwIsXLzBt2jTk5eXhp59+gomJCS5cuIDw8HDo6upKv5gk//8Rtqq8eqgi5C+RSJCdnY3p06dLpyodHR2Rn5+P0NBQTJ8+HY0aNVJ5/hUh91OnTuH48ePYu3dvqTGo62f/LolEgrlz5+Lvv/9GeHi4zMJwdfzsJRKJXPlU1s8eeFtITZ48Gbq6uli6dKnMPiG/90rCYoxIIFZWVli+fHmZ7apVq1bq/v79+2P58uVISEgo8qVUODRva2uLrl27olevXggKCkJYWBgAICIiAv/88w9Onjwp/QHk4OAAAAgLC8OwYcNgbm4u3ZeRkQF9fX1p//n5+cjMzJT+Rq2IipB/YV6dOnWSOc7FxQWhoaG4fv06GjVqpPL8hc49NzcX3333HUaMGIFatWrhxYsXAIC8vDzk5+fjxYsX0NPTg46Ojtp+9u9asGAB9u7di6CgIHTs2FFmn7p99sDbnB4+fFikz8KRnsKcKutnn5mZiYkTJyIlJQWbN29GnTp1ZPYL+b1XEhZjRAKpXbs2+vbtW+5+5P3tTU9PD1ZWVtLpCwC4fv06LCwsilwibm1tjfz8fNy7dw/m5uYya0nq1q0rbVd4C4jS7l9VkoqQv5WVFQ4cOCDt4/0+NTTeLqtVdf5C5/769Ws8evQI69evx/r164u0b9++PQIDAzFp0iS1/ewLLVmyBFu3bsWiRYtkFrQXUrfPHgCaNWuGI0eO4NWrVzL33ypcI1WYc2X87F+/fo3JkycjMTERmzZtKnYkVMjvvZJwAT9RJffrr78CePtbcGkyMzNx/fp1NGzYULrN1NQUSUlJSEtLk2l75coVAIC5uTkAwM7ODiYmJkWmtPbs2QMdHZ0iI0sfU3ny79atG0QiEU6dOiXT9vTp0xCJRNIrsSpq/srmrq+vj02bNhX506JFCzRt2hSbNm1Cv379AFTc3IHyffYAsGLFCqxfvx5ff/01hg4dWuyxFTX/8v69z8/Px8GDB6XbJBIJ9u3bh1atWklHkipq7kDx+WdnZ8PHxwfXr19HREQEWrZsWeyxFfF7jyNjRJXEo0ePMGvWLPTt2xeWlpbIy8tDbGwstm/fjk8//VRmqH7cuHFwcHBA8+bNYWBggAcPHiAqKgovXryAj4+PtJ2npyf27dsHLy8vTJgwAcbGxrhw4QIiIiLQuXNnNGrUCACgpaWFgIAAzJ07F+bm5ujcuTMSEhKwfv16TJw4sdhLyitD/s2bN8fQoUMRGhoKiUQCGxsb/Pnnn1i7di08PDykN8kUOn9V566lpQVHR8ci56lZsyays7Nl9gmd+4fIHwDWrl2LNWvWoE+fPrCxsUFCQoJ0X40aNaSjHkLn/yFyb9GiBfr374/FixcjLy8PjRs3xp49e3DlyhVERERI2wmdu6L5+/n54fz58wgMDIRIJJL5TOvUqSMtMivi955I8v74PBFVSBkZGfjqq69w48YNPHv2DBKJBI0aNcLAgQMxduxYaGn93+9WwcHBOHHiBJKSkvD69WsYGxujXbt28Pb2RosWLWT6TUhIQFhYGK5fv46XL1+ifv366NOnD7y8vIqs29ixYwd++eUX6WNBPD09MWHCBOl0XmXMPzc3F2vWrMHu3bvx9OlTmJubY8iQIZg8ebL0jt1C5/+hcn/fqFGjkJ2dXezjkNTtsx81ahQuXLhQ7PkcHBwQGRkps03dPvvs7GwEBwfj119/RUZGBpo2bQo/Pz9069atSAyV5bMXi8Ul9jN16lT4+flJX1e07z0WY0REREQC4poxIiIiIgGxGCMiIiISEIsxIiIiIgGxGCMiIiISEIsxIiIiIgGxGCMiIiISEIsxIiIiIgGxGCMiIiISEIsxIiKBhISElHrXcGXFxcVBLBYjLi5OoeNGjRqFUaNGqTweReTl5aFnz55YuXKldJuy+Shjzpw5+Oyzzz74eYjexWKMiIgqjOjoaKSkpGDs2LGCnN/HxwdXrlzBsWPHBDk/VU0sxoiIqEIoKCjA2rVr0b9/fxgZGQkSQ4MGDdCpUyf8/PPPgpyfqiYWY0REVCGcOXMGjx8/xoABAwSNo2/fvrhy5Qr++ecfQeOgqoPFGBGRir1+/RrBwcHo2bMnrK2t4ezsjC+//BJPnjwp89hRo0bB1dUV9+7dg5eXF+zt7eHo6IjvvvsOWVlZRdrHxcVh2LBhaN26NVxdXbFixQrk5eWpLBeJRILIyEj0798fNjY2cHR0xIwZM3D//v0ibTMzM7F48WK4ubnB2toabm5uWLJkCTIzM+U612+//Ybq1avD3t5ervb37t3D9OnT4ejoCBsbG/Tv3x+bN28utu3+/fvRt29f2NjYoFu3bvjll1+wa9cuiMViJCUlybR1cXGRxkP0MWgJHQARkTrJycnBuHHjcOPGDQwdOhRWVlZ4/PgxtmzZggsXLiAmJgYmJial9pGdnY1x48ahXbt2+OKLL5CQkIAtW7YgKSkJERER0nZ//vknvLy8UKtWLXh7e0NXVxcxMTE4ffq0yvL5/vvvERkZCScnJ3h4eCAlJQWRkZE4f/48du3aBQsLC2neY8eOxdWrVzF48GBYW1vj77//xvr163H58mVERUVBW1u71HNdvHgRrVq1goZG2eMEDx48wPDhw5Gbm4uRI0fC1NQUv/32GxYsWICHDx9i9uzZ0rZ79+7FzJkzYWVlhRkzZiAnJwdRUVEwNDQstm8TExNYWlri0qVLCrxTRMpjMUZEpEKbNm3C1atXERUVJTPC06tXLwwZMgTr169HYGBgqX2kp6dj6NChmDlzJgBgxIgRqFWrFtavX49Tp06hc+fOAIAlS5ZAW1sb0dHRqFOnDgBg+PDhKpvmu337NiIjI9GlSxesXr1aWiR17doVnp6eWL58OZYvXw4A2LlzJ65evYrAwEBMmjRJ2kfjxo0RFBSEXbt2wdPTs8Rz5efn4969e2jTpo1csS1fvhwZGRnYtm0b7OzsALx9n7y9vbFhwwYMGzYMTZs2RV5eHpYtWwYLCwts27YN1atXBwB4eHigZ8+eJfZvaWmJ27dvyxULUXlxmpKISIUOHDiAVq1aoWHDhkhNTZX+MTc3R8OGDfH777/L1c+YMWNkXo8bNw4AcPLkSQDA8+fPceXKFfTt21daiAFAjRo1Si16FHHixAkAwMSJE2VGq+zs7ODg4ICTJ0+ioKBA2lZfXx+jR4+W6WP06NGoVq0ajh8/Xuq50tPTIZFIShyteld+fj5OnToFR0dHaSEGABoaGpg4cSIkEok09r///hspKSnw8PCQFmIAUKtWLfTv37/EcxgZGSEtLa3MWIhUgSNjREQqlJiYiDdv3sDZ2bnY/fXq1Suzj+rVq8PMzExmm7m5OapXry5d3/Tw4UMAQJMmTYocX9w2ZRSeq2nTpkX2NWvWDHFxcUhNTUXt2rWRlJQECwsL6OnpybTT09ODhYVFkXVZ5ZGamoqsrCw0a9asyL7CWAvP9+jRIwBvR+je16hRoxLPUVhkEn0MLMaIiFSooKAA9vb2mDZtWrH7dXR0yuxDJBIVu10ikRT5/5Lafizynr+sdkZGRhCJRMjIyFBFWNLzvfueKeLFixcwNjZWSSxEZWExRkSkQo0aNUJ6ejo6dOigdB+ZmZl4+vSpzOhYcnIysrKypAvmLS0tAQB37twpcnxiYqLS535X4bnu3LmDdu3ayey7c+cO9PX1pQWLhYUFLl26hOzsbOjq6krbZWdn49GjR2jfvn2p59LU1ESjRo2kI36lMTExgb6+frFrugpzL4y98L93794t0vbevXslnuPBgwfFjrwRfQhcM0ZEpEL9+vXD3bt3ERMTU2SfRCJBamqqXP1s3LhR5vX69esBAG5ubgCA2rVro3Xr1jhw4IDMLTMyMzOxbds2ZcOX0a1bNwDA2rVrZabt/vrrL8TFxaFr167StWTdunVDVlYWoqKiZPqIjIxEVlaWtK/StGvXDn///XeZU4Sampro0qUL4uLi8Ndff0m3F940ViQSSd8na2trmJqaYvv27Xj16pW07fPnz7Fv375i+09NTUVSUhLatm1bZsxEqsCRMSIiFRo3bhxOnz6NOXPmIDY2Fm3btoWWlhaSkpJw4sQJ9OrVC/7+/qX2YWhoiAMHDiAlJQW2trZISEjA3r174eLiIr2SEgBmzZqFsWPHYvjw4fD09ISOjg5iYmJgaGgoXStVHk2bNsWoUaMQGRmJ8ePHo1u3bnj27BkiIyNhaGgok8eQIUOwa9cu/PDDD7hz54701hYxMTGws7PD4MGDyzxfjx49sGPHDvz5559FRuLe5+/vj3PnzmHs2LEYNWoUateujePHj+P8+fMYN26cdO2YlpYWvvjiC8yaNQuenp5wd3dHTk4Otm/fjgYNGuDatWtFplALbw3So0cPRd8yIqWwGCMiUiEdHR2sX78eGzZswP79+xEbGwstLS3UqVMHHTt2RN++fcvsQ09PDxs2bMDChQvx448/QkdHB56envjyyy9l2rVr1w4REREICgpCWFgYjIyMMHjwYDg4OGD8+PEqyefrr79Gw4YNER0djaVLl6J69ero1KkTAgICpFOAhXlv2LABwcHBOHLkCH799VfUrl0bY8aMgZ+fX5n3GAOATp06oX79+ti3b1+ZxViDBg0QHR2NFStWYNu2bXj16hUaNWqEb775BiNGjJBpO2jQIIhEIoSHh2P58uWoU6cORo8eDU1NTVy7dq3IRQcHDx6Era3tB3mIO1FxRBJlVzcSEZHKjRo1Cvfv31fpjVsrky1btuDHH3/EiRMnPvjzKRcuXIidO3fizz//hKamJoC3a8V69eqF4OBgdO/e/YOen6gQ14wREVGF4eHhAVNTU+kaOVXIyckpsg7t6dOn2LNnDxwcHKSFGACsWbMGrVu3ZiFGHxWnKYmI1FhOTk6Zt4vQ1tb+4KNQ8tLS0sKRI0dU2uc///yDL774Ar1790a9evXw6NEj7NixAzk5OfDz85Np+7///U+l5yaSB4sxIiI1dvny5SJ3xX+fg4MDIiMjP1JEH5+ZmRnEYjF27dqFtLQ06Orqws7ODn5+fmjdurXQ4RFxzRgRkTrLyMjAtWvXSm1Ts2ZNWFtbf6SIiOh9LMaIiIiIBMQF/EREREQCYjFGREREJCAWY0REREQCYjFGREREJCAWY0REREQCYjFGREREJCAWY0REREQC+n+xk2mQZEH8eAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df = az.compare(\n",
+    "    {\n",
+    "        \"nhefs_binary_linear\": idata_nhefs,\n",
+    "        \"nhefs_bart_cate\": idata_nhefs_cate,\n",
+    "        \"nhefs_rho_0\": idata_nhefs_0_rho,\n",
+    "        \"nhefs_s_s\": idata_nhefs_s_s,\n",
+    "    },\n",
+    "    var_name=\"likelihood_outcome\",\n",
+    ")\n",
+    "\n",
+    "az.plot_compare(compare_df);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters are well identified across all model specifications. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
nhefs_binary_linearalpha6.8190.4216.0227.5770.0120.0061324.02339.01.01
rho-0.1750.025-0.220-0.1270.0010.000931.02193.01.01
nhefs_bart_catealpha8.4680.5747.3959.5540.0240.014593.0970.01.00
rho-0.2350.040-0.311-0.1620.0020.001463.0812.01.00
nhefs_rho_0alpha5.1700.3724.4795.8810.0070.0052808.02768.01.00
rho-0.0000.001-0.0020.0020.0000.0007234.02770.01.00
nhefs_slate_slabalpha6.6830.4265.8327.4400.0140.007994.01915.01.00
rho-0.1700.025-0.216-0.1230.0010.000772.01334.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "nhefs_binary_linear alpha  6.819  0.421   6.022    7.577      0.012    0.006   \n",
+       "                    rho   -0.175  0.025  -0.220   -0.127      0.001    0.000   \n",
+       "nhefs_bart_cate     alpha  8.468  0.574   7.395    9.554      0.024    0.014   \n",
+       "                    rho   -0.235  0.040  -0.311   -0.162      0.002    0.001   \n",
+       "nhefs_rho_0         alpha  5.170  0.372   4.479    5.881      0.007    0.005   \n",
+       "                    rho   -0.000  0.001  -0.002    0.002      0.000    0.000   \n",
+       "nhefs_slate_slab    alpha  6.683  0.426   5.832    7.440      0.014    0.007   \n",
+       "                    rho   -0.170  0.025  -0.216   -0.123      0.001    0.000   \n",
+       "\n",
+       "                           ess_bulk  ess_tail  r_hat  \n",
+       "nhefs_binary_linear alpha    1324.0    2339.0   1.01  \n",
+       "                    rho       931.0    2193.0   1.01  \n",
+       "nhefs_bart_cate     alpha     593.0     970.0   1.00  \n",
+       "                    rho       463.0     812.0   1.00  \n",
+       "nhefs_rho_0         alpha    2808.0    2768.0   1.00  \n",
+       "                    rho      7234.0    2770.0   1.00  \n",
+       "nhefs_slate_slab    alpha     994.0    1915.0   1.00  \n",
+       "                    rho       772.0    1334.0   1.00  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat(\n",
+    "    {\n",
+    "        \"nhefs_binary_linear\": az.summary(idata_nhefs, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"nhefs_bart_cate\": az.summary(idata_nhefs_cate, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"nhefs_rho_0\": az.summary(idata_nhefs_0_rho, var_names=[\"alpha\", \"rho\"]),\n",
+    "        \"nhefs_slate_slab\": az.summary(idata_nhefs_s_s, var_names=[\"alpha\", \"rho\"]),\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "resulting in a treatment effect estimate greater than the baseline OLS assumption. We can also assess the Bayes factor i.e. the comparison of each model under a particular null hypothesis. Here we compare each model to the null of the OLS estimate. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "arviz - WARNING - The reference value is outside of the posterior. This translate into infinite support for H1, which is most likely an overstatement.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(2, 2, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "\n",
+    "feature_str = \" + \".join(features)\n",
+    "formula = \"outcome ~ trt +\" + feature_str\n",
+    "res = smf.ols(\n",
+    "    formula,\n",
+    "    df_nhefs,\n",
+    ").fit()\n",
+    "ols_est = res.params[\"trt\"]\n",
+    "\n",
+    "az.plot_bf(idata_nhefs, var_name=\"alpha\", ref_val=ols_est, ax=axs[0])\n",
+    "az.plot_bf(idata_nhefs_cate, var_name=\"alpha\", ref_val=ols_est, ax=axs[1])\n",
+    "az.plot_bf(idata_nhefs_0_rho, var_name=\"alpha\", ref_val=ols_est, ax=axs[2])\n",
+    "az.plot_bf(idata_nhefs_s_s, var_name=\"alpha\", ref_val=ols_est, ax=axs[3])\n",
+    "axs[0].set_xlabel(\"\"\" alpha \\n Linear Model \"\"\")\n",
+    "axs[1].set_xlabel(\"\"\" alpha \\n BART CATE Model \"\"\")\n",
+    "axs[2].set_xlabel(\"\"\" alpha \\n Rho at 0 Model \"\"\")\n",
+    "axs[3].set_xlabel(\"\"\" alpha \\n Linear Spike and Slab \"\"\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results all indicate a positive effect on weight due to the quitting smoking. They vary slightly in the attributed effect but, interestingly even if we try to zero out the correlation between treatment and outcome the model still implies a higher effect than observed in the simpler regression model. The Bayes factor plots report that the alternative hypothesis $\\alpha \\neq 3.3$ is between 5 and 40 times more likely than the null hypothesis of  $\\alpha = 3.3$. They also indicate the effect of Bayesian updating by the extent in which the posterior has transformed from the prior in each plot. Interestingly, these array of results suggests the treatment estimate is sensitive to the endogeneity correction. Allowing $\\rho \\neq 0$ increases the estimated effect (from ~5 to ~6). That means the joint model is using the estimated correlation between unobservables to further adjust the treatment coefficient beyond what the model with $\\rho = 0$ does. To top this off, the sign of the $\\rho$ estimate matters. When $\\rho$ is allowed to vary, the posterior estimate forces the correlation between treatment propensity and outcome measure negative. In other words, the propensity to quit is associated with _less weight gain_. By modelling this relationship as a correlation, the $\\alpha$ parameter is not forced to reflect this pattern, and the model can attribute a higher effect to the treatment intervention than we found with the OLS estimate. \n",
+    "\n",
+    "### Applying These Methods\n",
+    "\n",
+    "The models demonstrated here are not recipes to be followed mechanically but frameworks for making structural assumptions explicit. Before fitting a Bayesian causal model to real data, ask yourself three questions:\n",
+    "\n",
+    "**First: Can I defend my causal structure theoretically?** Which variables do you believe are confounders, which are instruments, which are irrelevant? Write down your causal graph before writing down your priors. If you cannot justify exclusion restrictions through domain knowledge or institutional understanding, data-driven variable selection will not rescue you—it will merely dress speculation in statistical clothing.\n",
+    "\n",
+    "**Second: How sensitive are my conclusions to structural assumptions?** The confounding parameter $\\rho$ is rarely identified from observables alone. Vary your priors on ρ across plausible ranges and observe how your treatment effect estimate shifts. Fit models with normal priors, sparse priors, and theory-driven exclusions. If your causal conclusions are stable across specifications, they're robust. If they vary dramatically, that variation is real epistemic uncertainty and should be reported as such.\n",
+    "\n",
+    "**Third: Where have I placed flexibility, and why?** Automated variable selection and nonparametric methods are powerful tools, but flexibility in the outcome equation can absorb the causal effects you're trying to estimate. As we demonstrated with BART, sufficiently flexible outcome models learn total associations rather than structural parameters. Use flexibility in the treatment equation if needed, but keep the outcome equation constrained to interpretable causal parameters.\n",
+    "\n",
+    "### Conclusion\n",
+    "\n",
+    "These questions point to what distinguishes structural modeling from purely associational approaches. When we specify a Bayesian causal model, we write down a probabilistic program that encodes our beliefs about how data are generated—which variables influence which, how uncertainty enters, what exclusions hold. Once fitted, the model becomes a working machine we can run forward under interventions, perturb in its assumptions, and interrogate for consequences. This executable character lets us simulate alternative worlds and test the coherence of our causal story, rather than merely report coefficients.\n",
+    "\n",
+    "The virtue of treating causal models as probabilistic programs is twofold. First, it forces us to articulate our causal beliefs explicitly i.e. the graphical, functional, and stochastic components that make the model run. Second, it offers a disciplined way to explore what follows from those beliefs under uncertainty. Bayesian structural causal inference therefore unites an epistemic modesty with computational rigor: each model is a local, provisional machine for generating causal understanding, not a final map of the world.\n",
+    "\n",
+    "The credibility revolution's achievement was recognizing that causal claims require more than correlations. Causal inference requires identification strategies. These strategies try to bracket complexity through design. Bayesian structural modeling takes a complementary path: it models complexity explicitly, then explores how robust our conclusions are to structural perturbations. Both approaches succeed when we know not only how our models work, but where they stop working. \n",
+    "\n",
+    "Every causal model, like every fish tank, is a \"small world\" whose regularities we can nurture but never universalize. Our task is not to master the ocean, but to build clear tanks and learn when to change the water.\n",
+    "\n",
+    "### References\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "CausalPy",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/references.bib b/docs/source/references.bib
index 207d0e21..9460e6c4 100644
--- a/docs/source/references.bib
+++ b/docs/source/references.bib
@@ -23,6 +23,17 @@ @article{richardson2009monetary
   publisher={The University of Chicago Press}
 }
 
+
+@book{pearl2000causality,
+  title={Causality: Models, Reasoning, and Inference},
+  author={Pearl, J.},
+  isbn={9780521773621},
+  lccn={99042108},
+  url={https://books.google.ie/books?id=wnGU_TsW3BQC},
+  year={2000},
+  publisher={Cambridge University Press}
+}
+
 @book{angrist2014mastering,
   title={Mastering 'Metrics: The path from cause to effect},
   author={Angrist, Joshua D and Pischke, J{\"o}rn-Steffen},
@@ -30,6 +41,16 @@ @book{angrist2014mastering
   publisher={Princeton University Press}
 }
 
+@book{angrist2009mostly,
+  title={Mostly Harmless Econometrics: An Empiricist's Companion},
+  author={Angrist, J.D. and Pischke, J.S.},
+  isbn={9780691120355},
+  lccn={2008036265},
+  url={https://books.google.ie/books?id=YSAzEAAAQBAJ},
+  year={2009},
+  publisher={Princeton University Press}
+}
+
 @article{carpenter2009effect,
   title={The effect of alcohol consumption on mortality: regression discontinuity evidence from the minimum drinking age},
   author={Carpenter, Christopher and Dobkin, Carlos},
@@ -59,6 +80,13 @@ @book{hansenEconometrics
   publisher={Princeton}
 }
 
+@book{kaplan_bs_social_science,
+  title={Bayesian Statistics for the social sciences},
+  author={Kaplan, David},
+  year={2024},
+  publisher={Guilford Press}
+}
+
 @book{aronowFoundations,
   author={Aronow, P and  Miller, B},
   title={Foundations of Agnostic Statistics},
diff --git a/environment.yml b/environment.yml
index 09850e05..bf0fcee4 100644
--- a/environment.yml
+++ b/environment.yml
@@ -16,4 +16,5 @@ dependencies:
   - statsmodels
   - xarray>=v2022.11.0
   - pymc-extras>=0.3.0
+  - pymc-bart
   - python>=3.11