/00.2-Full-empirical-analysis-skill_Stata

name: Full-empirical-analysis-skill-Stata
description: Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".
triggers:
  - Stata empirical analysis
  - full Stata pipeline
  - reproducible do-file
  - Stata do-file workflow
  - reghdfe two-way FE
  - high-dimensional fixed effects Stata
  - ivreg2 weak instruments
  - ivregress 2sls liml gmm
  - csdid Callaway SantAnna
  - did_imputation Borusyak
  - eventstudyinteract Sun Abraham
  - sdid synthetic DID Stata
  - rdrobust Stata
  - rddensity manipulation test
  - synth synthetic control
  - synth_runner placebo
  - psmatch2 propensity score
  - teffects psmatch
  - teffects ipwra AIPW Stata
  - ebalance entropy balancing
  - xtreg fe re hausman
  - ppmlhdfe Poisson
  - quantile regression qreg
  - heckman selection model
  - esttab publication table
  - outreg2 LaTeX
  - estout coefplot
  - marginsplot interaction
  - rdplot binned scatter
  - binscatter Stata
  - bacondecomp Goodman Bacon
  - honestdid Rambachan Roth
  - wild cluster bootstrap boottest
  - ritest randomization inference
  - rwolf Romano-Wolf
  - oster delta
  - winsor2 winsorize Stata
  - tabstat table 1
  - balancetable Stata
  - misstable patterns
  - destring dates
  - xtset panel
  # Mode A — Epidemiology / public health
  - epidemiology pipeline Stata
  - public health causal inference Stata
  - target trial emulation Stata
  - teffects ipw aipw ipwra
  - g-formula Stata
  - eltmle TMLE Stata
  - HAL-TMLE Stata
  - Mendelian randomization Stata
  - mrrobust mregger
  - MR-PRESSO Stata
  - MR-Egger weighted median Stata
  - STROBE TRIPOD reporting Stata
  - evalue sensitivity Stata
  - Kaplan-Meier AFT survival Stata
  - sts stcox streg strmst2
  - 流行病学 Stata
  - 公共健康 Stata
  # Mode B — ML causal inference
  - ML causal inference Stata
  - ddml double machine learning Stata
  - pdslasso ivlasso
  - crforest causal forest Stata
  - cforest Stata
  - meta-learner S T X R DR Stata
  - CATE distribution Stata
  - policy tree Stata
  - off-policy evaluation Stata
  - conformal causal Stata
  - causal discovery PC Stata
  - 因果机器学习 Stata

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 counterpar