import numpy as np
import pandas as pd
from sklearn import linear_model
from dowhy.causal_estimator import CausalEstimate
from dowhy.causal_estimators.propensity_score_estimator import PropensityScoreEstimator
[docs]class PropensityScoreWeightingEstimator(PropensityScoreEstimator):
""" Estimate effect of treatment by weighing the data by
inverse probability of occurrence.
Straightforward application of the back-door criterion.
Supports additional parameters that can be specified in the estimate_effect() method.
- 'weighting_scheme': This is the name of weighting method to use. Can be inverse propensity score ("ips_weight", default), stabilized IPS score ("ips_stabilized_weight"), or normalized IPS score ("ips_normalized_weight")
"""
def __init__(self, *args, min_ps_score=0.05, max_ps_score=0.95, **kwargs):
super().__init__(*args, **kwargs)
self.logger.info("INFO: Using Propensity Score Weighting Estimator")
self.symbolic_estimator = self.construct_symbolic_estimator(self._target_estimand)
self.logger.info(self.symbolic_estimator)
if not hasattr(self, "weighting_scheme"):
self.weighting_scheme = 'ips_weight' # 'ips_weight', 'ips_normalized_weight', 'ips_stabilized_weight'
self.min_ps_score = min_ps_score
self.max_ps_score = max_ps_score
def _estimate_effect(self, recalculate_propensity_score=False):
if self._propensity_score_model is None or recalculate_propensity_score is True:
self._propensity_score_model = linear_model.LogisticRegression()
self._propensity_score_model.fit(self._observed_common_causes, self._treatment)
self._data['ps'] = self._propensity_score_model.predict_proba(self._observed_common_causes)[:,1]
# trim propensity score weights
self._data['ps'] = np.minimum(self.max_ps_score, self._data['ps'])
self._data['ps'] = np.maximum(self.min_ps_score, self._data['ps'])
# ips ==> (isTreated(y)/ps(y)) + ((1-isTreated(y))/(1-ps(y)))
# nips ==> ips / (sum of ips over all units)
# icps ==> ps(y)/(1-ps(y)) / (sum of (ps(y)/(1-ps(y))) over all control units)
# itps ==> ps(y)/(1-ps(y)) / (sum of (ps(y)/(1-ps(y))) over all treatment units)
ipst_sum = sum(self._data[self._treatment_name[0]] / self._data['ps'])
ipsc_sum = sum((1 - self._data[self._treatment_name[0]]) / (1-self._data['ps']))
num_units = len(self._data[self._treatment_name[0]])
num_treatment_units = sum(self._data[self._treatment_name[0]])
num_control_units = num_units - num_treatment_units
# Vanilla IPS estimator
self._data['ips_weight'] = (1/num_units) * (
self._data[self._treatment_name[0]] / self._data['ps'] +
(1 - self._data[self._treatment_name[0]]) / (1 - self._data['ps'])
)
self._data['tips_weight'] = (1/num_treatment_units) * (
self._data[self._treatment_name[0]] +
(1 - self._data[self._treatment_name[0]]) * self._data['ps']/ (1 - self._data['ps'])
)
self._data['cips_weight'] = (1/num_control_units) * (
self._data[self._treatment_name[0]] * (1 - self._data['ps'])/ self._data['ps'] +
(1 - self._data[self._treatment_name[0]])
)
# Also known as the Hajek estimator
self._data['ips_normalized_weight'] = (
self._data[self._treatment_name[0]] / self._data['ps'] / ipst_sum +
(1 - self._data[self._treatment_name[0]]) / (1 - self._data['ps']) / ipsc_sum
)
ipst_for_att_sum = sum(self._data[self._treatment_name[0]])
ipsc_for_att_sum = sum((1-self._data[self._treatment_name[0]])/(1 - self._data['ps'])*self._data['ps'] )
self._data['tips_normalized_weight'] = (
self._data[self._treatment_name[0]]/ ipst_for_att_sum +
(1 - self._data[self._treatment_name[0]]) * self._data['ps'] / (1 - self._data['ps']) / ipsc_for_att_sum
)
ipst_for_atc_sum = sum(self._data[self._treatment_name[0]] / self._data['ps'] * (1-self._data['ps']))
ipsc_for_atc_sum = sum((1 - self._data[self._treatment_name[0]]))
self._data['cips_normalized_weight'] = (
self._data[self._treatment_name[0]] * (1 - self._data['ps']) / self._data['ps'] / ipst_for_atc_sum +
(1 - self._data[self._treatment_name[0]])/ipsc_for_atc_sum
)
# Stabilized weights
p_treatment = sum(self._data[self._treatment_name[0]])/num_units
self._data['ips_stabilized_weight'] = (1/num_units) * (
self._data[self._treatment_name[0]] / self._data['ps'] * p_treatment +
(1 - self._data[self._treatment_name[0]]) / (1 - self._data['ps']) * (1- p_treatment)
)
self._data['tips_stabilized_weight'] = (1/num_treatment_units) * (
self._data[self._treatment_name[0]] * p_treatment +
(1 - self._data[self._treatment_name[0]]) * self._data['ps'] / (1 - self._data['ps']) * (1- p_treatment)
)
self._data['cips_stabilized_weight'] = (1/num_control_units) * (
self._data[self._treatment_name[0]] * (1 - self._data['ps']) / self._data['ps'] * p_treatment +
(1 - self._data[self._treatment_name[0]])* (1-p_treatment)
)
# Simple normalized estimator (commented out for now)
#ips_sum = self._data['ips_weight'].sum()
#self._data['nips_weight'] = self._data['ips_weight'] / ips_sum
#self._data['ips2'] = self._data['ps'] / (1 - self._data['ps'])
#treated_ips_sum = (self._data['ips2'] * self._data[self._treatment_name[0]]).sum()
#control_ips_sum = (self._data['ips2'] * (1 - self._data[self._treatment_name[0]])).sum()
#self._data['itps_weight'] = self._data['ips2'] / treated_ips_sum
#self._data['icps_weight'] = self._data['ips2'] / control_ips_sum
if self._target_units == "ate":
weighting_scheme_name = self.weighting_scheme
elif self._target_units == "att":
weighting_scheme_name = "t" + self.weighting_scheme
elif self._target_units == "atc":
weighting_scheme_name = "c" + self.weighting_scheme
else:
raise ValueError("Target units string value not supported")
# Calculating the effect
self._data['d_y'] = (
self._data[weighting_scheme_name] *
self._data[self._treatment_name[0]] *
self._data[self._outcome_name]
)
self._data['dbar_y'] = (
self._data[weighting_scheme_name] *
(1 - self._data[self._treatment_name[0]]) *
self._data[self._outcome_name]
)
est = self._data['d_y'].sum() - self._data['dbar_y'].sum()
# TODO - how can we add additional information into the returned estimate?
estimate = CausalEstimate(estimate=est,
target_estimand=self._target_estimand,
realized_estimand_expr=self.symbolic_estimator,
propensity_scores = self._data["ps"])
return estimate
[docs] def construct_symbolic_estimator(self, estimand):
expr = "b: " + ",".join(estimand.outcome_variable) + "~"
# TODO -- fix: we are actually conditioning on positive treatment (d=1)
var_list = estimand.treatment_variable + estimand.backdoor_variables
expr += "+".join(var_list)
return expr