Individual skill
causal-inference
Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."
skills/23-Learning-Bayesian-Statistics-baygent-skills/causal-inference/SKILL.md
Source commit 8684584e7d1127940f018157d2c579974fae8f79
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- Observed behavior
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- Last checked
- Nov 10, 2026
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Repository context
- Repository stars (not skill ratings)
- 5,006
- Repository last updated
- Nov 9, 2026
- License
- NOASSERTION
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- design Derived category
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README.mddoc · 4,528 bytesSKILL.mdcore · 8,238 bytesSKILL.mdcore · 8,238 bytesSKILL.mdcore · 8,238 bytesreferences/dags-and-identification.mddoc · 8,556 bytesreferences/quasi-experiments.mddoc · 19,268 bytesreferences/refutation.mddoc · 22,685 bytesreferences/reporting.mddoc · 11,013 bytesreferences/structural-models.mddoc · 7,650 bytes
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