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DoWhy (Microsoft) — causal inference library. Causal graph modeling, identification (back-door, front-door, IV), estimation (matching, IPW, double-ML), and refutation/robustness checks for causal claims.
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| name | dowhy |
| description | DoWhy (Microsoft) — causal inference library. Causal graph modeling, identification (back-door, front-door, IV), estimation (matching, IPW, double-ML), and refutation/robustness checks for causal claims. |
| tags | ["dowhy","causal-inference","causal-graph","identification","estimation","microsoft","zorai"] |
DoWhy (Microsoft/py-why) provides end-to-end causal inference: causal graph modeling (DAG), identification strategies (back-door, front-door, instrumental variables), estimation (linear regression, matching, IV, double-ML), and refutation tests (placebo, bootstrap, random common cause, data subset).
uv pip install dowhy
from dowhy import CausalModel
model = CausalModel(
data=df,
treatment="treatment",
outcome="outcome",
common_causes=["age", "gender", "income"],
)
# 1. Identify
identified = model.identify_effect(proceed_when_unidentifiable=True)
# 2. Estimate
estimate = model.estimate_effect(identified, method_name="backdoor.linear_regression")
print(f"ATE: {estimate.value:.4f} (p={estimate.p_value:.4f})")
# 3. Refute
refute = model.refute_estimate(identified, estimate, method_name="placebo_treatment_refuter")
print(f"Refutation passed: {refute.refutation_result}")