alterlab-ieu/alterlab-academic-skills/alterlab-causal-inference

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

Estimator map (verified libraries, pinned)

A stdlib router that maps the design to the estimator + its assumption + the verified call: scripts/estimator_router.py. Fuller worked patterns and diagnostics: references/estimators.md.

The mandatory two steps around every estimate

1. Before: write the identifying assumption in one sentence and how you will defend it

(pre-trend plot for DiD, first-stage F and an exclusion argument for IV, density/McCrary test for RDD, overlap/common-support for PSM). No assumption, no causal estimate.

1. After: run a refutation/robustness check — DoWhy.refute_estimate (random common cause,

placebo treatment, data subset), a pre-trend/event-study plot, a bandwidth-sensitivity for RDD, or an E-value / sensitivity analysis for unmeasured confounding. Report it next to the estimate.

Output Template

DESIGN + ASSUMPTION: <e.g. DiD; parallel trends, defended by the pre-2019 event-study plot>
ESTIMATOR:           <library.call(...), pinned version>
ESTIMATE:            <point estimate, 95% CI, clustered SE — never a bare p-value>
DIAGNOSTIC:          <first-stage F / density test / pre-trends / overlap>
REFUTATION:          <placebo / random-common-cause / bandwidth sensitivity result>
CLAIM SCOPE:         causal IFF the assumption + diagnostics hold; else associational

Quality Standards

• Report effect sizes with confidence intervals and the SE structure (clustered where relevant).
• Never present a causal estimate without its diagnostic and at least one refutation.
• Hand the final estimate + assumption to alterlab-ssci-inference-gate for the claim audit.
• No fabricated flags: every call above is verified against the library's current docs; if a

flag is unverified in your installed version, check --help/docs, do not guess.

References

• references/estimators.md — per-design worked calls, diagnostics, and refutation menu.
• scripts/estimator_router.py — stdlib design→estimator+assumption router.

Part of the AlterLab Academic Skills suite.