import numpy as np
import pandas as pd
from dowhy.causal_estimator import CausalEstimator
[docs]class PropensityScoreEstimator(CausalEstimator):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# We need to initialize the model when we create any propensity score estimator
self._propensity_score_model = None
# Check if the treatment is one-dimensional
if len(self._treatment_name) > 1:
error_msg = str(self.__class__) + "cannot handle more than one treatment variable"
raise Exception(error_msg)
# Checking if the treatment is binary
if not pd.api.types.is_bool_dtype(self._data[self._treatment_name[0]]):
error_msg = "Propensity score methods are applicable only for binary treatments"
self.logger.error(error_msg)
raise Exception(error_msg)
self.logger.debug("Back-door variables used:" +
",".join(self._target_estimand.backdoor_variables))
self._observed_common_causes_names = self._target_estimand.backdoor_variables
if self._observed_common_causes_names:
self._observed_common_causes = self._data[self._observed_common_causes_names]
# Convert the categorical variables into dummy/indicator variables
# Basically, this gives a one hot encoding for each category
# The first category is taken to be the base line.
self._observed_common_causes = pd.get_dummies(self._observed_common_causes, drop_first=True)
else:
self._observed_common_causes = None
error_msg = "No common causes/confounders present. Propensity score based methods are not applicable"
self.logger.error(error_msg)
raise Exception(error_msg)
[docs] def construct_symbolic_estimator(self, estimand):
'''
A symbolic string that conveys what each estimator does.
For instance, linear regression is expressed as
y ~ bx + e
'''
raise NotImplementedError
def _estimate_effect(self, recalculate_propensity_score=False):
'''
A custom estimator based on the way the propensity score estimates are to be used.
Parameters
-----------
recalculate_propensity_score: bool, default False,
This forces the estimator to recalculate the estimate for the propensity score.
'''
raise NotImplementedError