Fix deprecated args and dowhy wrapper

econml/dowhy.py

@@ -38,14 +38,18 @@ def _get_params(self):
         # to represent
         init_signature = inspect.signature(init)
         parameters = init_signature.parameters.values()
+        params = []
         for p in parameters:
             if p.kind == p.VAR_POSITIONAL or p.kind == p.VAR_KEYWORD:
                 raise RuntimeError("cate estimators should always specify their parameters in the signature "
                                    "of their __init__ (no varargs, no varkwargs). "
                                    f"{self._cate_estimator} with constructor {init_signature} doesn't "
                                    "follow this convention.")
+            # if the argument is deprecated, ignore it
+            if p.default != "deprecated":
+                params.append(p.name)
         # Extract and sort argument names excluding 'self'
-        return sorted([p.name for p in parameters])
+        return sorted(params)
 
     def fit(self, Y, T, X=None, W=None, Z=None, *, outcome_names=None, treatment_names=None, feature_names=None,
             confounder_names=None, instrument_names=None, graph=None, estimand_type="nonparametric-ate",
@@ -106,30 +110,41 @@ def fit(self, Y, T, X=None, W=None, Z=None, *, outcome_names=None, treatment_nam
         -------
         self
         """
-
-        Y, T, X, W, Z = check_input_arrays(Y, T, X, W, Z)
-
-        # create dataframe
-        n_obs = Y.shape[0]
-        Y, T, X, W, Z = reshape_arrays_2dim(n_obs, Y, T, X, W, Z)
-
-        # currently dowhy only support single outcome and single treatment
-        assert Y.shape[1] == 1, "Can only accept single dimensional outcome."
-        assert T.shape[1] == 1, "Can only accept single dimensional treatment."
-
         # column names
         if outcome_names is None:
             outcome_names = get_input_columns(Y, prefix="Y")
         if treatment_names is None:
             treatment_names = get_input_columns(T, prefix="T")
         if feature_names is None:
-            feature_names = get_input_columns(X, prefix="X")
+            if X is not None:
+                feature_names = get_input_columns(X, prefix="X")
+            else:
+                feature_names = []
         if confounder_names is None:
-            confounder_names = get_input_columns(W, prefix="W")
+            if W is not None:
+                confounder_names = get_input_columns(W, prefix="W")
+            else:
+                confounder_names = []
         if instrument_names is None:
-            instrument_names = get_input_columns(Z, prefix="Z")
+            if Z is not None:
+                instrument_names = get_input_columns(Z, prefix="Z")
+            else:
+                instrument_names = []
         column_names = outcome_names + treatment_names + feature_names + confounder_names + instrument_names
+
+        # transfer input to numpy arrays
+        Y, T, X, W, Z = check_input_arrays(Y, T, X, W, Z)
+        # transfer input to 2d arrays
+        n_obs = Y.shape[0]
+        Y, T, X, W, Z = reshape_arrays_2dim(n_obs, Y, T, X, W, Z)
+        # create dataframe
         df = pd.DataFrame(np.hstack((Y, T, X, W, Z)), columns=column_names)
+
+        # currently dowhy only support single outcome and single treatment
+        assert Y.shape[1] == 1, "Can only accept single dimensional outcome."
+        assert T.shape[1] == 1, "Can only accept single dimensional treatment."
+
+        # call dowhy
         self.dowhy_ = CausalModel(
             data=df,
             treatment=treatment_names,

econml/dr/_drlearner.py

@@ -1523,7 +1523,7 @@ def n_crossfit_splits(self, value):
 
     @property
     def criterion(self):
-        return self.criterion
+        return "mse"
 
     @criterion.setter
     def criterion(self, value):
@@ -1533,7 +1533,7 @@ def criterion(self, value):
 
     @property
     def max_leaf_nodes(self):
-        return self.max_leaf_nodes
+        return None
 
     @max_leaf_nodes.setter
     def max_leaf_nodes(self, value):

econml/tests/test_dowhy.py

@@ -5,7 +5,7 @@
 import unittest
 from econml.dml import LinearDML, CausalForestDML
 from econml.orf import DROrthoForest
-from econml.dr import DRLearner
+from econml.dr import DRLearner, ForestDRLearner
 from econml.metalearners import XLearner
 from econml.iv.dml import DMLATEIV
 from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso
@@ -33,6 +33,7 @@ def clf():
                                    linear_first_stages=False),
                   "dr": DRLearner(model_propensity=clf(), model_regression=reg(),
                                   model_final=reg()),
+                  "forestdr": ForestDRLearner(model_propensity=clf(), model_regression=reg()),
                   "xlearner": XLearner(models=reg(), cate_models=reg(), propensity_model=clf()),
                   "cfdml": CausalForestDML(model_y=reg(), model_t=clf(), discrete_treatment=True),
                   "orf": DROrthoForest(n_trees=10, propensity_model=clf(), model_Y=reg()),