Reduce dependencies

.github/workflows/advanced-on-demand.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
         poetry-version: [1.5.1]
         test-group: [1, 2, 3, 4]
 
@@ -54,6 +54,12 @@ jobs:
         with:
           poetry-version: ${{ matrix.poetry-version }}
 
+      - name: Install LLVM and Clang
+        uses: KyleMayes/install-llvm-action@v2
+        with:
+          version: "14.0"
+          directory: ${{ runner.temp }}/llvm
+
       - name: Install graphviz
         run: |
           sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config

.github/workflows/ci-install.yml

@@ -26,7 +26,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v4
@@ -36,6 +36,12 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
 
+    - name: Install LLVM and Clang
+      uses: KyleMayes/install-llvm-action@v2
+      with:
+        version: "14.0"
+        directory: ${{ runner.temp }}/llvm
+
     - name: Install graphviz
       run: |
         sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config

.github/workflows/ci.yml

@@ -27,7 +27,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
         poetry-version: [1.5.1]
         test-group: [1, 2, 3, 4, 5, 6]
 
@@ -44,6 +44,12 @@ jobs:
       with:
         poetry-version: ${{ matrix.poetry-version }}
 
+    - name: Install LLVM and Clang
+      uses: KyleMayes/install-llvm-action@v2.0.5
+      with:
+        version: "14.0"
+        directory: ${{ runner.temp }}/llvm
+
     - name: Install graphviz
       run: |
         sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config

.github/workflows/docs-ci.yml

@@ -20,6 +20,17 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Install LLVM and Clang
+        uses: KyleMayes/install-llvm-action@v2.0.5
+        with:
+          version: "14.0"
+          directory: ${{ runner.temp }}/llvm
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.12
+
       - name: Install Python Dependencies
         run: poetry install -E plotting -E pydot -E pygraphviz -E econml --with docs
 

.github/workflows/docs.yml

@@ -24,6 +24,17 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Install LLVM and Clang
+        uses: KyleMayes/install-llvm-action@v2.0.5
+        with:
+          version: "14.0"
+          directory: ${{ runner.temp }}/llvm
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.12
+
       - name: Install Python Dependencies
         run: poetry install -E plotting -E pydot -E pygraphviz -E econml --with docs
 

.github/workflows/nightly-tests.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, "3.10", "3.11"]
+        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
         poetry-version: [1.5.1]
 
     steps:
@@ -27,6 +27,12 @@ jobs:
         with:
           poetry-version: ${{ matrix.poetry-version }}
 
+      - name: Install LLVM and Clang
+        uses: KyleMayes/install-llvm-action@v2
+        with:
+          version: "14.0"
+          directory: ${{ runner.temp }}/llvm
+
       - name: Install graphviz
         run: |
           sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config

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
 --------------------------------

dowhy/causal_estimators/econml.py

@@ -5,7 +5,6 @@
 
 import numpy as np
 import pandas as pd
-from numpy.distutils.misc_util import is_sequence
 
 from dowhy.causal_estimator import CausalEstimate, CausalEstimator
 from dowhy.causal_identifier import IdentifiedEstimand
@@ -295,7 +294,6 @@ def shap_values(self, df: pd.DataFrame, *args, **kwargs):
     def apply_multitreatment(self, df: pd.DataFrame, fun: Callable, *args, **kwargs):
         ests = []
         assert not isinstance(self._treatment_value, str)
-        assert is_sequence(self._treatment_value)
 
         if df is None:
             filtered_df = None