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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.
Passed 20 of 22 test cases with the skill, and 18 of 22 without it.
2 fixed18 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
How do I install the standard Python library for causal inference that supports graph modeling, identification, estimation, and refutation tests?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends running uv pip install dowhy | Missed | Passed |
The instructions your model reads when the skill loads.
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}")
test cases passed, vs 18 / 22 without
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