Comments (3)
from dowhy.
thanks for the clarification.
from dowhy.
I have a similar question:
estimates = model.estimate_effect(estimands, method_name = "backdoor.linear_regression")
print(estimate)
Output:
linear_regression
{'control_value': 0, 'treatment_value': 1, 'test_significance': None, 'evaluate_effect_strength': False, 'confidence_intervals': False, 'target_units': 'ate', 'effect_modifiers': []}
*** Causal Estimate ***
Identified estimand
Estimand type: nonparametric-ate
Estimand : 1
Estimand name: backdoor
Estimand expression:
d
─────────────────(E[Target])
d[X₁ X₂ X₃ X₄]
Estimand assumption 1, Unconfoundedness: If U→{X1,X2,X3,X4} and U→Target then P(Target|X1,X2,X3,X4,,U) = P(Target|X1,X2,X3,X4,)
Realized estimand
b: Target~X1+X2+X3+X4
Target units: ate
Estimate
Mean value: 7.982444233679164
What is Mean value: 7.982444233679164?
from dowhy.
Related Issues (20)
- Support polars data frames HOT 2
- What is the purpose of providing observation data in gcm.conventional_samples()?
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- Can you provide code demo for each function? HOT 2
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from dowhy.