Full-empirical-analysis-skill-Stata

Installation
SKILL.md

Full Empirical Analysis — Classical Stata Workflow

This skill is the canonical 8-step pipeline an applied economist runs on every empirical paper, written in the traditional Stata ecosystem — native Stata + the 20+ community commands that have become de-facto standards (reghdfe, ivreg2, csdid, did_imputation, eventstudyinteract, sdid, rdrobust, rddensity, synth, synth_runner, psmatch2, teffects, ebalance, coefplot, esttab, outreg2, boottest, ritest, rwolf, bacondecomp, honestdid, binscatter).

Companion skills: if the user wants the same pipeline in Python, route to 00-StatsPAI_skill (agent-native DSL) or 00.1-Full-empirical-analysis-skill (explicit Python stack). This skill is the Stata counterpart — every step produces a .do file you can hand to a journal's replication office or a co-author who refuses to leave Stata.

Philosophy

  1. Stata idioms, not Python-translated. reghdfe, not "statsmodels analogue of reghdfe". esttab, not "Stata's stargazer".
  2. Reproducible .do files. Every code block below is runnable after use data.dta, clear. No Jupyter, no notebooks — just do-files and log files.
  3. Full pipeline, not just regressions. Stata users historically over-invest in Step 5 (modeling) and under-invest in Steps 1–4 and 6–8. This skill treats them as first-class.
  4. Rich outputs. Every step yields at least one table (.tex/.rtf) or figure (.pdf/.png) — never a coefficient printed to the Results window and forgotten.
  5. Progressive disclosure. SKILL.md gives the canonical command at each step; references/ holds variant-specific depth (dozens of tests, estimator-specific diagnostics, graph recipes).

Three domain modes (default = AER econ; alternates = epi & ML-causal)

The default playbook above is AER-style applied econometrics — the AEA convention: written-out estimating equation, identifying assumption, design horse-race, full robustness gauntlet. The skill also ships two parallel sub-pipelines for the other two big causal-inference traditions, each reusing the same Steps 1–4 (cleaning / construction / Table 1 / diagnostics) and Step 8 (tables/figures) — only Step 5 (estimator) and Step 6/7 swap commands:

Installs
32
GitHub Stars
1.7K
First Seen
Apr 28, 2026
Full-empirical-analysis-skill-Stata — brycewang-stanford/awesome-agent-skills-for-empirical-research