Remove DeepIV

Signed-off-by: Keith Battocchi <kebatt@microsoft.com>

Author: kbattocchi

.github/workflows/ci.yml

@@ -118,7 +118,7 @@ jobs:
         kind: [except-customer-scenarios, customer-scenarios]
         include:
         - kind: "except-customer-scenarios"
-          extras: "[tf,plt,ray]"
+          extras: "[plt,ray]"
           pattern: "(?!CustomerScenarios)"
           install_graphviz: true
           version: '3.8' # no supported version of tensorflow for 3.9
@@ -226,16 +226,16 @@ jobs:
             extras: ""
           - kind: other
             opts: '-m "cate_api and not ray" -n auto'
-            extras: "[tf,plt]"
+            extras: "[plt]"
           - kind: dml
             opts: '-m "dml and not ray"'
-            extras: "[tf,plt]"
+            extras: "[plt]"
           - kind: main
             opts: '-m "not (notebook or automl or dml or serial or cate_api or treatment_featurization or ray)" -n 2'
-            extras: "[tf,plt,dowhy]"
+            extras: "[plt,dowhy]"
           - kind: treatment
             opts: '-m "treatment_featurization and not ray" -n auto'
-            extras: "[tf,plt]"
+            extras: "[plt]"
           - kind: ray
             opts: '-m "ray"'
             extras: "[ray]"
@@ -308,13 +308,13 @@ jobs:
         kind: [tests]
         os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
-        extras: ["[tf,plt,dowhy,ray]"]
+        extras: ["[plt,dowhy,ray]"]
         include:
           # explicitly add the two notebook extras
           - kind: notebooks
             os: ubuntu-latest
             python-version: '3.8'
-            extras: "[tf,plt,ray]"
+            extras: "[plt,ray]"
           - kind: notebooks
             os: ubuntu-latest
             python-version: '3.9'

README.md

@@ -415,36 +415,6 @@ lb, ub = est.effect_interval(X_test, alpha=0.05) # OLS confidence intervals
 ```
 </details>
 
-<details>
-<summary>Deep Instrumental Variables (click to expand)</summary>
-
-```Python
-import keras
-from econml.iv.nnet import DeepIV
-
-treatment_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(2,)),
-                                    keras.layers.Dropout(0.17),
-                                    keras.layers.Dense(64, activation='relu'),
-                                    keras.layers.Dropout(0.17),
-                                    keras.layers.Dense(32, activation='relu'),
-                                    keras.layers.Dropout(0.17)])
-response_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(2,)),
-                                  keras.layers.Dropout(0.17),
-                                  keras.layers.Dense(64, activation='relu'),
-                                  keras.layers.Dropout(0.17),
-                                  keras.layers.Dense(32, activation='relu'),
-                                  keras.layers.Dropout(0.17),
-                                  keras.layers.Dense(1)])
-est = DeepIV(n_components=10, # Number of gaussians in the mixture density networks)
-             m=lambda z, x: treatment_model(keras.layers.concatenate([z, x])), # Treatment model
-             h=lambda t, x: response_model(keras.layers.concatenate([t, x])), # Response model
-             n_samples=1 # Number of samples used to estimate the response
-             )
-est.fit(Y, T, X=X, Z=Z) # Z -> instrumental variables
-treatment_effects = est.effect(X_test)
-```
-</details>
-
 See the <a href="#references">References</a> section for more details.
 
 ### Interpretability

doc/map.svg

@@ -86,16 +86,6 @@ Doubly Robust (DR) IV
     econml.iv.dr.IntentToTreatDRIV
     econml.iv.dr.LinearIntentToTreatDRIV
 
-.. _deepiv_api:
-
-DeepIV
-^^^^^^
-
-.. autosummary::
-    :toctree: _autosummary
-
-    econml.iv.nnet.DeepIV
-
 .. _tsls_api:
 
 Sieve Methods

doc/reference.rst

@@ -9,8 +9,6 @@ Detailed estimator comparison
 +=============================================+==============+==============+==================+=============+=================+============+==============+====================+
 | :class:`.SieveTSLS`                         | Any          | Yes          |                  | Yes         | Assumed         | Yes        | Yes          |                    |
 +---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
-| :class:`.DeepIV`                            | Any          | Yes          |                  |             |                 | Yes        | Yes          |                    |
-+---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
 | :class:`.SparseLinearDML`                   | Any          |              | Yes              | Yes         | Assumed         | Yes        | Yes          | Yes                |
 +---------------------------------------------+--------------+--------------+------------------+-------------+-----------------+------------+--------------+--------------------+
 | :class:`.SparseLinearDRLearner`             | Categorical  |              | Yes              |             | Projected       |            | Yes          | Yes                |

doc/spec/comparison.rst

@@ -14,6 +14,5 @@ of [Newey2003]_.
 .. toctree::
     :maxdepth: 2
 
-    estimation/deepiv.rst
     estimation/two_sls.rst
     estimation/orthoiv.rst

doc/spec/estimation/deepiv.rst

@@ -1,4 +1,4 @@
 # Copyright (c) PyWhy contributors. All rights reserved.
 # Licensed under the MIT License.
 
-__all__ = ["dml", "dr", "nnet", "sieve"]
+__all__ = ["dml", "dr", "sieve"]