def _estimate_effect(self):
upper_limit = self.rd_threshold_value + self.rd_bandwidth
lower_limit = self.rd_threshold_value - self.rd_bandwidth
rows_filter = np.s_[(self.rd_variable >= lower_limit)
& (self.rd_variable <= upper_limit)]
local_rd_variable = self.rd_variable[rows_filter]
local_treatment_variable = self._treatment[rows_filter]
local_outcome_variable = self._outcome[rows_filter]
local_df = pd.DataFrame(
data={
'local_rd_variable': local_rd_variable,
'local_treatment': local_treatment_variable,
'local_outcome': local_outcome_variable
})
iv_estimator = InstrumentalVariableEstimator(
local_df,
self._target_estimand, ['local_treatment'], ['local_outcome'],
test_significance=self._significance_test,
params={'iv_instrument_name': 'local_rd_variable'})
est = iv_estimator.estimate_effect()
return est
def _estimate_effect(self):
upper_limit = self.rd_threshold_value + self.rd_bandwidth
lower_limit = self.rd_threshold_value - self.rd_bandwidth
rows_filter = np.s_[(self.rd_variable >= lower_limit)
& (self.rd_variable <= upper_limit)]
local_rd_variable = self.rd_variable[rows_filter]
local_treatment_variable = self._treatment[self._treatment_name[0]][
rows_filter] # indexing by treatment name again since this method assumes a single-dimensional treatment
local_outcome_variable = self._outcome[rows_filter]
local_df = pd.DataFrame(
data={
'local_rd_variable': local_rd_variable,
'local_treatment': local_treatment_variable,
'local_outcome': local_outcome_variable
})
print(local_df)
iv_estimator = InstrumentalVariableEstimator(
local_df,
self._target_estimand, ['local_treatment'], ['local_outcome'],
test_significance=self._significance_test,
iv_instrument_name='local_rd_variable')
est = iv_estimator.estimate_effect()
return est