from dowhy.do_sampler import DoSampler
from dowhy.utils.propensity_score import state_propensity_score
[docs]class MultivariateWeightingSampler(DoSampler):
def __init__(self, data,
*args, params=None, variable_types=None,
num_cores=1, keep_original_treatment=False,
causal_model=None,
**kwargs):
"""
g, df, data_types
"""
super().__init__(data,
params=params,
variable_types=variable_types, num_cores=num_cores,
keep_original_treatment=keep_original_treatment,
causal_model=causal_model)
self.logger.info("Using MultivariateWeightingSampler for do sampling.")
self.logger.info("Caution: do samplers assume iid data.")
self.point_sampler = False
[docs] def make_treatment_effective(self, x):
to_sample = self._df.copy()
if not self.keep_original_treatment:
for treatment, value in x.items():
to_sample = to_sample[to_sample[treatment] == value]
self._df = to_sample
[docs] def disrupt_causes(self):
self._df['state_propensity'] = state_propensity_score(self._data,
self._target_estimand.backdoor_variables,
self._treatment_names,
variable_types=self._variable_types)
self._df['weight'] = self.compute_weights()
[docs] def sample(self):
self._df = self._df.sample(len(self._data), replace=True, weights=self._df['weight'])
self._df.index = self._data.index
[docs] def compute_weights(self):
weights = 1. / self._df['state_propensity']
return weights