dowhy
SkillAI & modelsDoWhy (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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the dowhy skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/dowhy/SKILL.md and read by ahel’s review.
Overview
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).
Installation
uv pip install dowhy
Full Workflow
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}")
References
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
dowhy-mkurman- Source
- github.com/mkurman/zorai