def null_refutation_test(self,
data=None,
dataset="linear",
beta=10,
num_common_causes=1,
num_instruments=1,
num_samples=100000,
treatment_is_binary=True):
# Supports user-provided dataset object
if data is None:
data = dowhy.datasets.linear_dataset(
beta=beta,
num_common_causes=num_common_causes,
num_instruments=num_instruments,
num_samples=num_samples,
treatment_is_binary=treatment_is_binary)
model = CausalModel(data=data['df'],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["gml_graph"],
proceed_when_unidentifiable=True,
test_significance=None)
target_estimand = model.identify_effect()
ate_estimate = model.estimate_effect(
identified_estimand=target_estimand,
method_name=self.estimator_method,
test_significance=None)
true_ate = data["ate"]
# To test if there are any exceptions
ref = model.refute_estimate(
target_estimand,
ate_estimate,
method_name=self.refuter_method,
confounders_effect_on_treatment=self.confounders_effect_on_t,
confounders_effect_on_outcome=self.confounders_effect_on_y,
effect_strength_on_treatment=self.effect_strength_on_t,
effect_strength_on_outcome=self.effect_strength_on_y)
# To test if the estimate is identical if refutation parameters are zero
refute = model.refute_estimate(
target_estimand,
ate_estimate,
method_name=self.refuter_method,
confounders_effect_on_treatment=self.confounders_effect_on_t,
confounders_effect_on_outcome=self.confounders_effect_on_y,
effect_strength_on_treatment=0,
effect_strength_on_outcome=0)
error = abs(refute.new_effect - ate_estimate.value)
print(
"Error in refuted estimate = {0} with tolerance {1}%. Estimated={2},After Refutation={3}"
.format(error, self._error_tolerance * 100, ate_estimate.value,
refute.new_effect))
res = True if (
error < abs(ate_estimate.value) * self._error_tolerance) else False
assert res
model = CausalModel(
data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["dot_graph"])
identified_estimand = model.identify_effect()
estimate = model.estimate_effect(
identified_estimand,
method_name="backdoor.linear_regression")
print("Causal Estimate is " + str(estimate.value))
# Adding a random common cause variable
res_random = model.refute_estimate(
identified_estimand, estimate,
method_name="random_common_cause")
print(res_random)
# Replacing treatment with a random (placebo) variable
res_placebo = model.refute_estimate(
identified_estimand,
estimate,
method_name="placebo_treatment_refuter", placebo_type="permute")
print(res_placebo)
# Removing a random subset of the data
res_subset = model.refute_estimate(
identified_estimand,
estimate,
method_name="data_subset_refuter",
from dowhy.do_why import CausalModel
import dowhy.datasets
import Pygraphviz
# Load some sample data
data = dowhy.datasets.linear_dataset(beta=10,
num_common_causes=5,
num_instruments=2,
num_samples=10000,
treatment_is_binary=True)
# Create a causal model from the data and given graph.
model = CausalModel(
data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["dot_graph"],
)
# Identify causal effect and return target estimands
identified_estimand = model.identify_effect()
# Estimate the target estimand using a statistical method.
estimate = model.estimate_effect(
identified_estimand, method_name="backdoor.propensity_score_matching")
# Refute the obtained estimate using multiple robustness checks.
refute_results = model.refute_estimate(identified_estimand,
estimate,
method_name="random_common_cause")
