alterlab-causal-inference

name	alterlab-causal-inference
description	Estimates causal effects from observational and quasi-experimental data — difference-in-differences, instrumental variables, regression discontinuity, panel fixed effects, propensity-score / doubly-robust methods, and heterogeneous treatment effects (CATE) — using the verified Python stack: statsmodels and linearmodels (PanelOLS, IV2SLS), pyfixest (feols, event studies, Sun-Abraham, did2s), DoWhy (identify -> estimate -> refute), EconML (LinearDML, CausalForestDML, DRLearner), and rdrobust for RD. It names the identifying assumption before estimating and runs a refutation/robustness check after. Use when the request mentions difference-in-differences, instrumental variables, regression discontinuity, fixed effects / panel causal estimation, propensity scores, or treatment-effect estimation from non-randomized data. For choosing the design first prefer alterlab-ssci-design-gate; for plain regression or descriptive stats prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Bash(python:*)
compatibility	Requires (declare in-session, no runtime install on Anthropic API): statsmodels, linearmodels>=6, pyfixest>=0.25, dowhy>=0.12, econml>=0.15, rdrobust>=1.3 (pip). Runs locally via `uv run python`; no API key.
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-ssci-design-gate (design + assumption), alterlab-statsmodels, alterlab-statistical-analysis; audited by alterlab-ssci-inference-gate"}

Causal Inference — Name the Assumption, Estimate, Then Try to Break It

Skill type: ANALYSIS MODULE. Estimates a causal effect from data that was not fully randomized. The discipline is not the estimator — it is the identifying assumption the estimator relies on, stated before the fit and stress-tested after. If the design is not yet fixed, that belongs upstream in alterlab-ssci-design-gate.

Core Mission

EVERY CAUSAL ESTIMATE INHERITS AN ASSUMPTION. STATE IT, ESTIMATE UNDER IT, THEN REFUTE IT.

When to Use This Skill

• "Estimate the effect with difference-in-differences / an event study."
• "I have an instrument for the treatment — run instrumental variables / 2SLS."
• "There's a cutoff score — run a regression discontinuity."
• "Panel data with unit and time fixed effects — estimate the treatment effect."
• "Give me the heterogeneous treatment effect / CATE across subgroups."

Does NOT Trigger

The request is really about…	Route to	Why not this skill
Choosing the design & its identifying assumption	alterlab-ssci-design-gate	Design routing precedes estimation.
Plain OLS / descriptive or inferential stats (no causal identification)	alterlab-statistical-analysis / alterlab-statsmodels	No treatment-effect identification problem.
Latent-variable / SEM / factor structure	alterlab-sem-psychometrics	Measurement model, not treatment effect.
Auditing whether the final claim is licensed	alterlab-ssci-inference-gate	Claim audit, downstream.

Estimator map (verified libraries, pinned)

Design	Identifying assumption	Estimator (verified API)
DiD / event study	parallel trends	pyfixest (v0.60): `pf.feols("y ~ i(time, treat, ref)
Panel fixed effects	no time-varying confounders	linearmodels (v7): PanelOLS.from_formula("y ~ 1 + x + EntityEffects + TimeEffects", panel).fit(cov_type='clustered', cluster_entity=True).