Add docs + fix placebo_refuter

docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb

@@ -338,7 +338,6 @@
    "outputs": [],
    "source": [
     "import keras\n",
-    "from econml.deepiv import DeepIVEstimator\n",
     "dims_zx = len(model.get_instruments())+len(model.get_effect_modifiers())\n",
     "dims_tx = len(model._treatment)+len(model.get_effect_modifiers())\n",
     "treatment_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(dims_zx,)), # sum of dims of Z and X \n",
@@ -356,7 +355,7 @@
     "                                    keras.layers.Dense(1)])\n",
     "\n",
     "deepiv_estimate = model.estimate_effect(identified_estimand, \n",
-    "                                        method_name=\"iv.econml.deepiv.DeepIV\",\n",
+    "                                        method_name=\"iv.econml.iv.nnet.DeepIV\",\n",
     "                                        target_units = lambda df: df[\"X0\"]>-1, \n",
     "                                        confidence_intervals=False,\n",
     "                                method_params={\"init_params\":{'n_components': 10, # Number of gaussians in the mixture density networks\n",

dowhy/causal_estimators/causalml.py

@@ -160,6 +160,14 @@ def _get_causalml_class_object(self, module_method_name, *args, **kwargs):
     def estimate_effect(
         self, data: pd.DataFrame = None, treatment_value: Any = 1, control_value: Any = 0, target_units=None, **_
     ):
+        """
+        data: dataframe containing the data on which treatment effect is to be estimated.
+        treatment_value: value of the treatment variable for which the effect is to be estimated.
+        control_value: value of the treatment variable that denotes its absence (usually 0)
+        target_units: The units for which the treatment effect should be estimated.
+                     It can be a DataFrame that contains values of the effect_modifiers and effect will be estimated only for this new data.
+                     It can also be a lambda function that can be used as an index for the data (pandas DataFrame) to select the required rows.
+        """
         if data is None:
             data = self._data
 

dowhy/causal_estimators/econml.py

@@ -219,6 +219,14 @@ def _get_econml_class_object(self, module_method_name, *args, **kwargs):
     def estimate_effect(
         self, data: pd.DataFrame = None, treatment_value: Any = 1, control_value: Any = 0, target_units=None, **_
     ):
+        """
+        data: dataframe containing the data on which treatment effect is to be estimated.
+        treatment_value: value of the treatment variable for which the effect is to be estimated.
+        control_value: value of the treatment variable that denotes its absence (usually 0)
+        target_units: The units for which the treatment effect should be estimated.
+                     It can be a DataFrame that contains values of the effect_modifiers and effect will be estimated only for this new data.
+                     It can also be a lambda function that can be used as an index for the data (pandas DataFrame) to select the required rows.
+        """
         if data is None:
             data = self._data
         self._target_units = target_units

dowhy/causal_refuters/placebo_treatment_refuter.py

@@ -8,7 +8,7 @@
 from joblib import Parallel, delayed
 from tqdm.auto import tqdm
 
-from dowhy.causal_estimator import CausalEstimate, CausalEstimator
+from dowhy.causal_estimator import CausalEstimate
 from dowhy.causal_estimators.econml import Econml
 from dowhy.causal_estimators.instrumental_variable_estimator import InstrumentalVariableEstimator
 from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
@@ -206,6 +206,12 @@ def refute_placebo_treatment(
                 "Only placebo_type=''permute'' is supported for creating placebo for instrumental variable estimation methods."
             )
 
+    # For IV methods, the estimating_instrument_names should also be
+    # changed. Create a copy to avoid modifying original object
+    if isinstance(estimate, InstrumentalVariableEstimator):
+        estimate = copy.deepcopy(estimate)
+        estimate.iv_instrument_name = ["placebo_" + s for s in parse_state(estimate.iv_instrument_name)]
+
     # We need to change the identified estimand
     # We make a copy as a safety measure, we don't want to change the
     # original DataFrame