add readme add blanklines

Signed-off-by: Nathaniel <NathanielF@users.noreply.github.com>

Author: NathanielF

README.md

@@ -188,6 +188,17 @@ Instrumental Variable regression is an appropriate technique when you wish to es
 
 ![](https://raw.githubusercontent.com/pymc-labs/CausalPy/main/docs/source/_static/iv_reg1.png)
 
+
+### Inverse Propensity Score Weighting
+
+Propensity scores are often used to address the risks of bias or confounding introduced in an observational study by
+selection effects into the treatment condition. Propensity scores can be used in a number of ways, but here we demonstrate
+their usage within corrective weighting schemes aimed to recover as-if random allocation of subjects to the treatment condition.
+The technique "up-weights" or "down-weights" individual observations to better estimate a causal estimand such as the average treatment
+effect. 
+
+![](https://raw.githubusercontent.com/pymc-labs/CausalPy/main/docs/source/_static/propensity_weight.png)
+
 ## Learning resources
 
 Here are some general resources about causal inference:

causalpy/pymc_experiments.py

@@ -1568,6 +1568,8 @@ class InversePropensityWeighting(ExperimentalDesign, PropensityDataValidator):
     ...         },
     ...     ),
     ... )
+    <BLANKLINE>
+    <BLANKLINE>
     """
 
     def __init__(

causalpy/pymc_models.py

@@ -425,6 +425,8 @@ class PropensityScore(ModelBuilder):
     ...                 'obs_indx': np.arange(df.shape[0])
     ...                },
     ... )
+    <BLANKLINE>
+    <BLANKLINE>
     Inference...
     """  # noqa: W605