Remove some dependencies

Signed-off-by: Patrick Bloebaum <[email protected]>

.github/workflows/docs-ci.yml

@@ -26,16 +26,14 @@ jobs:
           version: "14.0"
           directory: ${{ runner.temp }}/llvm
 
-      - name: Some cleanup for more disk space
-        run: |
-          rm -rf /usr/share/dotnet
-          rm -rf "$AGENT_TOOLSDIRECTORY"
-          apt-get autoremove -y
-          apt-get clean
-          df -h
-
       - name: Install Python Dependencies
-        run: poetry install -E plotting -E pydot -E pygraphviz -E econml --with docs
+        run: poetry install
+
+      - name: Install Python Dependencies 2
+        run: poetry install -E plotting -E pydot -E pygraphviz
+
+      - name: Install Python Dependencies 2
+        run: poetry install -E econml --with docs
 
       - run: git config --global --add safe.directory /__w/dowhy/dowhy
 

.github/workflows/docs.yml

@@ -30,16 +30,14 @@ jobs:
           version: "14.0"
           directory: ${{ runner.temp }}/llvm
 
-      - name: Some cleanup for more disk space
-        run: |
-          rm -rf /usr/share/dotnet
-          rm -rf "$AGENT_TOOLSDIRECTORY"
-          apt-get autoremove -y
-          apt-get clean
-          df -h
-
       - name: Install Python Dependencies
-        run: poetry install -E plotting -E pydot -E pygraphviz -E econml --with docs
+        run: poetry install
+
+      - name: Install Python Dependencies 2
+        run: poetry install -E plotting -E pydot -E pygraphviz
+
+      - name: Install Python Dependencies 2
+        run: poetry install -E econml --with docs
 
       - run: git config --global --add safe.directory /__w/dowhy/dowhy
 

docs/source/dowhy.graph_learners.rst

@@ -4,14 +4,6 @@ dowhy.graph\_learners package
 Submodules
 ----------
 
-dowhy.graph\_learners.cdt module
---------------------------------
-
-.. automodule:: dowhy.graph_learners.cdt
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 dowhy.graph\_learners.ges module
 --------------------------------
 

docs/source/user_guide/modeling_causal_relations/learning_causal_structure.rst

@@ -5,16 +5,7 @@ Learning the causal graph is only necessary in case we cannot construct the grap
 
 In many cases, the true causal structure for a given dataset may not be known. We can utilize *graph discovery* algorithms to provide candidate causal graphs consistent with the dataset. Such graphs can then be inspected, edited and modified by a user to match their domain expertise or world knowledge. Note that these graphs should be not used directly because graph discovery from observational data is a provably impossible problem in the fully non-parametric setting. Given a dataset, there exist multiple graphs that  would lead to the exact same joint distribution and thus are indistinguishable based on the dataset (such graphs constitute the *Markov equivalence class*).  As a result, graph discovery algorithms make certain assumptions to learn a graph and do not guarantee the validity of a learned graph. 
 
-DoWhy does not implement graph discovery algorithms, but provides a simple way to input the learnt graph from a discovery algorithm. The only constraint is that DoWhy expects the algorithm to output a directed acyclic graph (DAG). In the future, we expect to support learning causal graphs directly through integration with the `causal-learn <https://github.com/py-why/causal-learn>`_ and `dodiscover <https://github.com/py-why/dodiscover>`_ packages in PyWhy. 
-
-Graph discovery using CDT
--------------------------
-Given a dataset as a pandas DataFrame, the following snippet learns the graph using LiNGAM algorithm and loads it in DoWhy. The algorithm implementation is in the Causal Discovery Toolbox (CDT) package which needs to be installed separately.
-
->>> from cdt.causality.graph import LiNGAM
->>> causal_graph = LiNGAM().predict(dataset)
-
-For a full example using CDT, you can refer to the :doc:`../../example_notebooks/dowhy_causal_discovery_example`. 
+DoWhy does not implement graph discovery algorithms, but provides a simple way to input the learnt graph from a discovery algorithm. The only constraint is that DoWhy expects a directed acyclic graph (DAG) in the form of a networkx graph. For packages that implement different causal discovery algorithms, see the `causal-learn <https://github.com/py-why/causal-learn>`_ and `dodiscover <https://github.com/py-why/dodiscover>`_ packages in PyWhy.
 
 Graph discovery using dodiscover
 --------------------------------

pyproject.toml

@@ -107,7 +107,6 @@ pytest-split = ">=0.8.0"
 nbformat = ">=5.4.0"
 jupyter = ">=1.0.0"
 flaky = ">=3.7.0"
-keras = ">=2.9.0"
 xgboost = ">=2.0.2"
 mypy = ">=1.7.1"
 torch = [
@@ -140,7 +139,6 @@ pydata-sphinx-theme = ">=0.9.0"
 ipykernel = ">=6.15.1"
 sphinx-copybutton = "0.5.0"
 seaborn = ">=0.12.1"
-cdt =">=0.6.0"
 
 #
 # Versions defined for security reasons

tests/causal_estimators/test_econml_estimator.py

@@ -144,7 +144,6 @@ def test_metalearners(self):
         assert pytest.approx(sl_cate_estimates_test_f[0], 0.01) != sl_cate_estimates_test_f[1]
 
     def test_iv_estimators(self):
-        keras = pytest.importorskip("keras")
         # Setup data
         data = datasets.linear_dataset(
             10,
@@ -168,37 +167,7 @@ def test_iv_estimators(self):
         # Test DeepIV
         dims_zx = len(model._instruments) + len(model._effect_modifiers)
         dims_tx = len(model._treatment) + len(model._effect_modifiers)
-        treatment_model = keras.Sequential(
-            [
-                keras.layers.Dense(128, activation="relu", input_shape=(dims_zx,)),  # sum of dims of Z and X
-                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=(dims_tx,)),  # sum of dims of T and X
-                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),
-            ]
-        )
-        dmliv_estimate = model.estimate_effect(
-            identified_estimand,
-            method_name="iv.econml.iv.dml.DMLIV",
-            target_units=lambda df: df["X0"] > -1,
-            confidence_intervals=False,
-            method_params={
-                "init_params": {"discrete_treatment": False, "discrete_instrument": False},
-                "fit_params": {},
-            },
-        )
+
         # Test IntentToTreatDRIV
         data = datasets.linear_dataset(
             10,