Full-empirical-analysis-skill-Stata

by brycewang-stanford4.6k

Communication

Install

The skill file stays in the publisher's repository.

Paste the copied command in a terminal. It installs this SKILL.md into that agent.

npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill pipeline -a cursor -y

Download skill plugins/empirical-analysis-stata/skills/pipeline/SKILL.md on GitHub

Related skills

View all

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".

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 / , E-value sensitivity via (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and (DML via / , S/T/X/R/DR meta-learners via and , causal forest via / , BART/BCF via / -style externals, CATE distribution + policy tree via , off-policy evaluation, conformal causal externals, fairness audit, DAG learning via / 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:

* Run once on a fresh Stata install: ssc install reghdfe, replace ssc install ftools, replace // dependency of reghdfe / ivreg2 ssc install ivreg2, replace ssc install ranktest, replace // dependency of ivreg2 ssc install ivreghdfe, replace // ivreg2 × reghdfe: high-dim FE IV ssc install ppmlhdfe, replace // Poisson with HD FE ssc install csdid, replace // Callaway–Sant'Anna (2021) ssc install drdid, replace // dependency of csdid ssc install did_imputation, replace // Borusyak–Jaravel–Spiess (2024) ssc install eventstudyinteract, replace // Sun & Abraham (2021) ssc install sdid, replace // Synthetic DID (Arkhangelsky et al. 2021) ssc install did_multiplegt_dyn, replace // de Chaisemartin & D'Haultfœuille ssc install bacondecomp, replace // Goodman-Bacon (2021) ssc install honestdid, replace // Rambachan–Roth (2023) PT sensitivity ssc install rdrobust, replace // Calonico–Cattaneo–Titiunik RD ssc install rddensity, replace // McCrary / Cattaneo et al. density test ssc install synth, replace // Abadie–Diamond–Hainmueller SCM ssc install synth_runner, replace // SCM with placebos + inference ssc install psmatch2, replace // propensity-score matching ssc install ebalance, replace // entropy balancing ssc install coefplot, replace ssc install estout, replace // provides estout / esttab / eststo ssc install outreg2, replace ssc install asdoc, replace // one-click Word/Excel tables ssc install binscatter, replace ssc install balancetable, replace ssc install winsor2, replace ssc install xtable, replace // better xtreg output tables ssc install boottest, replace // wild cluster bootstrap (Roodman et al.) ssc install ritest, replace // randomization inference ssc install rwolf, replace // Romano–Wolf multiple-testing ssc install moremata, replace // Mata extensions (dep for several) ssc install mdesc, replace // missing data description ssc install missings, replace // missings dropvars, report ssc install unique, replace // unique IDs in panel ssc install schemepack, replace // modern publication themes

* Build relative-time factor variable, base at e = -1 gen rel = year - first_treat replace rel = . if missing(first_treat) // never-treated dropped from event study keep if inrange(rel, -4, 4) | missing(first_treat) gen rel_p = rel + 5 // shift -4..4 → 1..9; ib4 means "base = -1" * (a) Sun-Abraham via -eventstudyinteract- (preferred for staggered adoption) forvalues k = 1/9 { gen rel_d`k' = (rel_p == `k') } eventstudyinteract log_wage rel_d1 rel_d2 rel_d3 rel_d5 rel_d6 rel_d7 rel_d8 rel_d9, /// cohort(first_treat) control_cohort(never_treated) /// absorb(worker_id year) vce(cluster worker_id) * (b) Coefficient figure with shaded CI + reference line at e=-1 coefplot, keep(rel_d*) vertical omitted /// yline(0, lpattern(dash) lcolor(gs10)) xline(4.5, lpattern(dash)) /// rename(rel_d1="-4" rel_d2="-3" rel_d3="-2" rel_d5="0" /// rel_d6="1" rel_d7="2" rel_d8="3" rel_d9="4") /// ciopts(recast(rcap)) levels(95) /// xtitle("Years relative to treatment") /// ytitle("Coefficient (ATT, 95% CI)") /// title("Figure 2a. Event-study coefficients (95% CI; ref. e = -1)") /// scheme(s2color) graph export "figures/fig2a_event_study.pdf", replace graph export "figures/fig2a_event_study.png", replace width(2400) * (c) Numerical pre-trends F-test (joint zero on the leads e = -4..-2) test rel_d1 rel_d2 rel_d3 display "Pre-trends F = " %5.2f r(F) " p = " %5.3f r(p) * (d) Bacon decomposition figure (Goodman-Bacon 2021) — TWFE diagnostic bacondecomp log_wage training, ddetail * The -bacondecomp- output names the contaminated 2×2 weights. * Save the auto-generated figure as figures/fig2a_bacon.pdf. * (e) Callaway-Sant'Anna dynamic ATT (when -csdid- is the main estimator) csdid log_wage age edu, ivar(worker_id) time(year) gvar(first_treat) /// method(dripw) agg(event) estat event, window(-4 4) csdid_plot, title("Figure 2a-bis. Dynamic ATT (Callaway-Sant'Anna)") graph export "figures/fig2a_csdid.pdf", replace

* 5a. OLS with cluster-robust SE reg log_wage training age edu tenure, vce(cluster firm_id) eststo ols_m1 * 5b. Two-way FE — reach for reghdfe (absorb HD FE; clusters any level) reghdfe log_wage training age edu tenure, /// absorb(worker_id year) vce(cluster worker_id) eststo fe_m1 * Multi-way clustering reghdfe log_wage training, absorb(worker_id year) /// vce(cluster worker_id firm_id) * High-dim FE (industry × year, firm × state) reghdfe log_wage training, absorb(worker_id i.industry#i.year) /// vce(cluster firm_id) * 5c. 2×2 DID reg log_wage i.treated##i.post age edu, vce(cluster worker_id) * Or with absorbed FE: reghdfe log_wage c.treated#c.post, absorb(worker_id year) /// vce(cluster worker_id) * 5d. Event study (dynamic DID, base period = -1) * Build relative-time factor var, base at k = -1: gen rel = rel_time + 10 // shift so -10..10 → 0..20 replace rel = 0 if missing(rel_time) // never-treated → bin 0 * (preferable: use -eventstudyinteract- or -did_imputation- — see reference 5.4) reghdfe log_wage ib9.rel, absorb(worker_id year) vce(cluster worker_id) coefplot, keep(*.rel) vertical omitted /// yline(0) xline(9.5, lpattern(dash)) /// xtitle("Years relative to treatment") ytitle("Coefficient (ATT)") graph export "figures/event_study.pdf", replace * 5e. Staggered DID — modern estimators (see references/05-modeling.md §5.4) * Callaway–Sant'Anna (2021): csdid log_wage, ivar(worker_id) time(year) gvar(first_treat) /// method(dripw) agg(group) * Sun & Abraham (2021): eventstudyinteract log_wage rel_dummies_*, /// cohort(first_treat) control_cohort(never_treated) /// absorb(worker_id year) vce(cluster worker_id) * Borusyak–Jaravel–Spiess (2024) imputation: did_imputation log_wage worker_id year first_treat, /// allhorizons pretrend(5) * Synthetic DID (Arkhangelsky et al. 2021): sdid log_wage worker_id year training, vce(bootstrap) /// graph g1on g1_opt(ytitle("log wage")) * 5f. IV / 2SLS — ivreg2 is the de-facto standard (full diagnostics) ivreg2 log_wage age edu (training = draft_lottery), /// cluster(firm_id) first endog(training) * ivreghdfe for HD FE with IV: ivreghdfe log_wage age (training = draft_lottery), /// absorb(worker_id year) cluster(worker_id) first * 5g. Sharp RD rdrobust outcome running_var, c(0) kernel(triangular) bwselect(mserd) rdplot outcome running_var, c(0) * Fuzzy RD rdrobust outcome running_var, c(0) fuzzy(treatment) * Density test (McCrary / Cattaneo et al.) rddensity running_var, c(0) * 5h. Binary outcome — always follow with margins logit employed training age edu, vce(cluster firm_id) margins, dydx(training) atmeans * 5i. Count outcome with HD FE ppmlhdfe citations training age, absorb(firm_id year) /// cluster(firm_id) * 5j. Quantile regression qreg log_wage training age edu, quantile(0.5) sqreg log_wage training age edu, quantile(0.1 0.25 0.5 0.75 0.9) reps(500)

* 6a. Progressive specifications (M1 → M6) eststo clear eststo m1: qui reg log_wage training, vce(cluster firm_id) eststo m2: qui reg log_wage training age edu, vce(cluster firm_id) eststo m3: qui reghdfe log_wage training age edu tenure, /// absorb(worker_id) vce(cluster worker_id) eststo m4: qui reghdfe log_wage training age edu tenure, /// absorb(worker_id year) vce(cluster worker_id) eststo m5: qui reghdfe log_wage training age edu tenure, /// absorb(worker_id year region) vce(cluster worker_id) eststo m6: qui reghdfe log_wage training age edu tenure, /// absorb(worker_id year i.industry#i.year) vce(cluster worker_id) esttab m1 m2 m3 m4 m5 m6 using "tables/table_main.tex", /// replace se star(* 0.10 ** 0.05 *** 0.01) /// stats(N r2 r2_a, labels("N" "R²" "Adj. R²")) /// label booktabs * 6b. Alternative cluster levels foreach c in worker_id firm_id industry state { qui reghdfe log_wage training, absorb(worker_id year) vce(cluster `c') display "cluster=`c' b=" _b[training] " se=" _se[training] } * 6c. Wild cluster bootstrap (few clusters) qui reghdfe log_wage training, absorb(worker_id year) vce(cluster state) boottest training, cluster(state) reps(9999) seed(42) * 6d. Subsample splits foreach mask in "female==0" "female==1" "age<40" "age>=40" { qui reghdfe log_wage training if `mask', /// absorb(worker_id year) vce(cluster worker_id) display "`mask': b=" _b[training] " se=" _se[training] " N=" e(N) } * 6e. Placebo timing gen fake_first = first_treat - 3 gen fake_post = (year >= fake_first) preserve keep if year < first_treat // drop real post-period reghdfe log_wage fake_post, absorb(worker_id year) /// vce(cluster worker_id) restore * 6f. Randomization inference (permutation) ritest training _b[training], reps(1000) seed(0) /// strata(worker_id): reghdfe log_wage training, /// absorb(worker_id year) vce(cluster worker_id) * 6g. Romano–Wolf multiple testing rwolf log_wage employed hours_worked, indepvar(training) /// controls(age edu) reps(500) seed(42) method(reghdfe) /// fe(worker_id year) cluster(worker_id) * 6h. TWFE bias diagnosis — Goodman-Bacon decomposition bacondecomp log_wage training, ddetail * 6i. Parallel trends sensitivity — HonestDiD * (after running the event study and saving b/V) honestdid, pre(1/4) post(5/9) mvec(0(0.1)0.5) * 6j. Oster (2019) δ* qui reg log_wage training // short scalar bs = _b[training]; scalar r2s = e(r2) qui reghdfe log_wage training age edu tenure, absorb(worker_id year) scalar bl = _b[training]; scalar r2l = e(r2) psacalc delta training, mcontrol(age edu tenure) rmax(1.3*r2l)

eststo clear * (1) Baseline eststo base: qui reghdfe log_wage training age edu tenure, /// absorb(industry year) vce(cluster firm_id) * (2) Drop top 1% of wage qui sum wage, detail eststo no99: qui reghdfe log_wage training age edu tenure if wage < r(p99), /// absorb(industry year) vce(cluster firm_id) * (3) Balanced panel only preserve keep if e(sample) // start from baseline sample bysort worker_id: egen n_obs = count(year) qui sum n_obs, detail keep if n_obs == r(max) eststo balpan: qui reghdfe log_wage training age edu tenure, /// absorb(industry year) vce(cluster firm_id) restore * (4) Drop early adopting cohorts eststo dropearly: qui reghdfe log_wage training age edu tenure if first_treat > 2008, /// absorb(industry year) vce(cluster firm_id) * (5) Worker FE absorbed (selection on FE-removable unobservables) eststo wfe: qui reghdfe log_wage training age edu tenure, /// absorb(worker_id year) vce(cluster firm_id) * (6) Two-way cluster (firm × year) eststo cl2way: qui reghdfe log_wage training age edu tenure, /// absorb(industry year) vce(cluster firm_id year) * (7) Wild cluster bootstrap (after running the baseline) qui reghdfe log_wage training age edu tenure, absorb(industry year) vce(cluster firm_id) boottest training, cluster(firm_id) reps(9999) seed(42) nograph // returns CI * Note bootstrapped p-value in the row footer of the master table. * (8) Log outcome eststo logy: qui reghdfe log_log_wage training age edu tenure, /// absorb(industry year) vce(cluster firm_id) * (9) IHS outcome (handles zeros) gen ihs_wage = asinh(wage) eststo ihsy: qui reghdfe ihs_wage training age edu tenure, /// absorb(industry year) vce(cluster firm_id) * (10) PSM-weighted outcome qui psmatch2 training age edu tenure, out(log_wage) n(1) common eststo psm: qui reg log_wage training age edu tenure if _support == 1, vce(cluster firm_id) * (11) Entropy balance ebalance training age edu tenure eststo ebal: qui reg log_wage training age edu tenure [pw=_webal], vce(cluster firm_id) esttab base no99 balpan dropearly wfe cl2way logy ihsy psm ebal /// using "tables/tableA1_robustness.tex", /// replace se star(* 0.10 ** 0.05 *** 0.01) label booktabs /// keep(training) /// mtitles("(1) Baseline" "(2) Drop top 1%" "(3) Balanced" "(4) Drop early" /// "(5) Worker FE" "(6) 2-way cluster" "(7) log Y" "(8) IHS Y" /// "(9) PSM" "(10) Entropy bal.") /// stats(N r2, labels("N" "R²")) /// addnotes("Each column is one robustness check. β̂ on training is the focal coefficient.")

Mode	Reader convention	Step-5 estimator stack	Reporting stack	Jump to
Default — Applied Econ (AER / QJE / AEJ)	"Show the equation + identifying assumption + design horse-race; controls visible; clustered SE"	DID / IV / RD / SCM / matching / `reghdfe` HDFE	AER house-style multi-column `esttab` / `outreg2` / `coefplot` + 8-section paper layout	Steps 1 → 8 (entire playbook below)
Mode A — Epidemiology / Public Health	"STROBE / TRIPOD-AI; target trial protocol; doubly-robust estimand; absolute & relative risk; KM survival"	Target-trial emulation · IPTW (`teffects ipw`) · IPWRA / AIPW (`teffects ipwra` / `teffects aipw`) · g-formula (`gformula`) · TMLE (`eltmle`) · Mendelian randomization (`mrrobust` IVW / `mregger` / `mrpresso`) · KM/Cox/AFT (`sts`/`stcox`/`streg`/`strmst2`)	Same `esttab` + risk-difference / hazard-ratio / E-value rows	§A. Epidemiology pipeline
Mode B — ML Causal Inference	"DML / meta-learners / causal forest / DR-learner; CATE distribution; policy value"	DML (`ddml` / `pdslasso`) · S/T/X/R/DR-Learner (`ddml interactive`) · GRF causal forest (`crforest` / `cforest`) · BART / BCF (external Python via `python_user`) · matrix completion (external)	`esttab` ML horse-race + `crforest` CATE plot + policy-value table	§B. ML causal pipeline

User says...	Mode the skill switches to
"Run a DID / IV / RD / event study", "AER table", "applied micro"	Default (AER econ) — Steps 1 → 8
"Target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE / TRIPOD", "公共健康 / 流行病学", "epi pipeline", "RWE study", "cohort study", "case-control"	Mode A (Epi) — §A
"DML", "double machine learning", "ddml", "causal forest", "crforest", "meta-learner", "CATE", "policy learning", "ML causal", "因果机器学习"	Mode B (ML causal) — §B
"Mix" (e.g. "estimate DID + then ML CATE on the heterogeneity")	Default + Mode B in sequence — every estimator stores results via `eststo`, drop them all into one `esttab` for the horse-race column

#	Table	Stata source	Saves to
T1	Summary statistics & balance (treated vs control, with SMD / p-values)	`balancetable` + `asdoc sum` (Step 3)	`tables/table1_balance.{tex,rtf,xlsx,docx}`
T2 ★	Main results — multi-column regression M1→M6 (progressive controls + FE)	`eststo` 6 specs → `esttab` (Step 5–6)	`tables/table2_main.{tex,rtf,xlsx,docx}`
T3	Mechanism / outcome ladder — same treatment, 3+ outcomes side-by-side	loop `eststo: reghdfe` over outcomes → `esttab` (Step 7)	`tables/table3_mechanism.{tex,rtf,xlsx,docx}`
T4	Heterogeneity — subgroup × main coef (gender, age, region, …)	subgroup `eststo` + `suest` Wald → `esttab` (Step 7)	`tables/table4_heterogeneity.{tex,rtf,xlsx,docx}`
T5	Robustness battery — alt SE / cluster / sample / placebo, in one table	`eststo` × variants → `esttab` (Step 6)	`tables/table5_robustness.{tex,rtf,xlsx,docx}`

#	Figure	Stata source	Saves to
F1	Trend / motivation — treated vs control over time, with policy line	`collapse (mean) y, by(year treat)` → `twoway line` (Step 3)	`figures/fig1_trend.pdf` (+ `.png`)
F2	Event-study coefficients with 95% CI, base period at –1	`eventstudyinteract` / `csdid` / `coefplot, keep(*.rel)` (Step 5)	`figures/fig2_event_study.pdf`
F3	Coefficient plot across specs M1→M6	`coefplot m1 m2 m3 m4 m5 m6, keep(treat) vertical` (Step 8)	`figures/fig3_coefplot.pdf`
F4	Robustness / sensitivity curve — `bacondecomp` plot, `honestdid` plot, or cluster-comparison forest	scenario-specific (Step 6)	`figures/fig4_sensitivity.pdf`

§	Artifact	Stata primitive	Filenames
§1	Figure 1: raw trends / treatment rollout	`collapse` + `twoway line` · `heatplot` for staggered rollout	`figures/fig1_trend.{pdf,png}`
§1	Table 1: summary stats (full / treated / control + Δ + SMD)	`balancetable` · `asdoc sum, by()` · `tabstat`	`tables/table1_balance.{tex,rtf,xlsx,docx}`
§3	Figure 2: identification graphic (event-study / first-stage / McCrary / RD scatter / SCM trajectory)	`coefplot` after `eventstudyinteract`/`csdid` · `binscatter` · `rdplot` · `rddensity, plot` · `synth`	`figures/fig2_event_study.{pdf,png}`
§4	Table 2: main results — progressive controls M1→M6	`eststo` 6 specs → `esttab`	`tables/table2_main.{tex,rtf,xlsx,docx}`
§4	Table 2-bis: design horse-race (OLS / IV / DID / matching)	`eststo` mix + `esttab`	`tables/table2b_designs.{tex,rtf,xlsx,docx}`
§4	Figure 3: coefficient plot across specs	`coefplot m1 m2 m3 m4 m5 m6, keep(treat)`	`figures/fig3_coefplot.{pdf,png}`
§5	Table 3: heterogeneity by subgroup	`eststo` per slice + `esttab` + `suest` Wald	`tables/table3_heterogeneity.{tex,rtf,xlsx,docx}`
§5	Figure 4: dose-response / margins-by-quartile	`xtile` + `margins` + `marginsplot`	`figures/fig4_dose.{pdf,png}`
§6	Table 4: mechanism / outcome ladder	loop `eststo: reghdfe` over outcomes → `esttab`	`tables/table4_mechanism.{tex,rtf,xlsx,docx}`
§7	Table A1: robustness master (one column per check)	`eststo` × variants → `esttab`	`tables/tableA1_robustness.{tex,rtf,xlsx,docx}`
§7	Figure 5: spec curve — coefficient + 95% CI across all specs	hand-rolled spec loop + `twoway rcap`	`figures/fig5_spec_curve.{pdf,png}`
§7	Figure 6: sensitivity (HonestDiD / Oster / E-value)	`honestdid, coefplot` · `psacalc plot` · `evalue` table	`figures/fig6_sensitivity.{pdf,png}`
§8	Replication bundle: all tables in one document	`esttab ..., append` to one `.tex` / `.rtf` · `texdoc`	`replication/paper_tables.{tex,rtf,xlsx,docx}`

Tier	Use when	API	Hot options
1. Single multi-column table	Exporting one Table 2 / Table 3 / Table A1 with progressive columns	`.tex` / `.rtf`: `esttab` with `booktabs``.xlsx` / `.docx`: `outreg2` (esttab's native Office export is a data dumper, not a publication formatter — outreg2 produces properly formatted Word/Excel with borders, aligned stars, and AER-style layout)	`esttab` for tex/rtf: `keep()`, `drop()`, `mtitles()`, `stats(N r2 r2_a, labels(...))`, `star(* 0.10 0.05 * 0.01)`, `label`, `booktabs`, `addnotes()`.`outreg2` for xlsx/docx: `label dec(3)`, `addtext()` for FE indicators, `replace` first / `append` subsequent columns.Always emit all four formats — esttab for tex/rtf in a `foreach ext in tex rtf` loop, outreg2 for xlsx/docx in a separate `foreach ext in xlsx docx` loop.
2. Multi-panel paper format (Tables 2 + 3 + A1 + A2 in one file)	Producing the paper-tables block — main + heterogeneity + robustness + placebo as a single document	`esttab ... using "paper.tex", replace` for first panel; subsequent `esttab ... using "paper.tex", append` for each next panel; `texdoc init "paper.tex"` for full LaTeX with prose. Repeat for `.rtf`/`.xlsx`/`.docx` bundles.	first panel: `replace`; subsequent: `append`; surround with `texdoc` for headings
3. Full session bundle (the Stata 17+ `collect` equivalent)	Replication appendix that mixes summary stats + balance + multiple regression tables + headings + prose in one file	`collect create papercollect get summary, ...collect get est ...collect layout ...collect export "paper.xlsx"` (also `.docx`/`.html`/`.tex`/`.md`)	Stata 17+ only; for older Stata use `texdoc` / `markdoc`

Design	Estimating equation	Identifying assumption	Stata estimator
2×2 DID	`Y_it = α_i + λ_t + β·D_it + X'γ + ε_it`	parallel trends conditional on X	`reghdfe Y c.D#c.post X, absorb(i t) vce(cluster i)`
Event-study (CS / SA)	`Y_it = α_i + λ_t + Σ_{e≠-1} β_e · 1{t-G_i = e} + ε_it`	no anticipation + group-time PT	`csdid` / `eventstudyinteract` / `did_imputation`
2SLS	`Y_i = α + β·D_i + X'γ + ε_i; D_i = π·Z_i + X'δ + u_i`	exclusion + relevance + monotonicity	`ivreg2` / `ivreghdfe`
Sharp RD	`Y_i = α + β·1{X_i ≥ c} + f(X_i) + ε_i` (local poly)	continuity of E[Y(0)\|X] at c, no manipulation	`rdrobust` (+ `rddensity`)
SCM	`Ŷ_1t(0) = Σ_j ŵ_j Y_jt`, τ_t = `Y_1t − Ŷ_1t(0)` for t≥T_0	pre-period fit + interpolation validity	`synth` / `synth_runner` / `sdid`
Selection-on-observables (matching/IPW)	`E[Y_i(d) \| X_i] = E[Y_i \| D=d, X_i]`	unconfoundedness + overlap	`teffects psmatch / ipw / aipw / ipwra`, `ebalance`

Test	Null	If rejected
`swilk` / `sktest`	residuals Normal	large N: usually ignore; small N: bootstrap
`estat hettest` / `imtest, white`	homoskedastic	use `vce(robust)` or `vce(cluster id)`
`xtserial` / `xttest3`	no panel autocorr / no groupwise hetero	cluster by unit
`xtcsd, pesaran`	no cross-sectional dependence	Driscoll–Kraay or `xtscc`
`estat vif` > 10	—	drop/combine collinear regressors
`estat ovtest`	specification OK	add polynomials / logs
`dfuller` reject + `kpss` fail to reject	stationary	keep levels
`dfuller` fail to reject	unit root	first-difference or cointegrate
`hausman`	RE consistent	use FE

Install

Related skills

Questions

Mode A — Epidemiology / public health

Mode B — ML causal inference

Full Empirical Analysis — Classical Stata Workflow

Philosophy

SkillOpt-style execution gate

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

Default Output Spec — Economics Empirical Paper

Required tables (always produced)

Required figures (always produced)

Output file layout (default)

When to deviate

Required packages

The 8 Steps — Canonical Pipeline (mapped to AER paper sections)

Paper-ready figure & table inventory (what to produce by section)

Export cookbook — LaTeX / Word / RTF in one block

Step −1 — Pre-Analysis Plan (pre-data; AEA RCT Registry style)

Step 0 — Sample-construction log & 5-check data contract

0.1 Sample-construction log (footnote 4)

0.2 Five-check data contract (go / no-go gate)

Step 1 — Data import & cleaning

Step 2 — Variable construction & transformation

Step 2.5 — Empirical strategy (write the equation + identifying assumption)

Equation × identifying assumption × Stata estimator (decision table)

Design picker (when the user is unsure)

Pre-registration strategy.do template

Step 3 — Descriptive statistics & Table 1

Step 3.5 — Identification graphics (Section "Identification, graphical evidence")

3.5.1 Event-study figure + numerical pre-trends test (DID identification)

3.5.2 First-stage F-statistic + scatter (IV identification)

3.5.3 RD: McCrary density + canonical RD plot

3.5.4 Matching: love plot (standardized differences pre vs post)

3.5.5 SCM: synthetic-control trajectory + gap plot

Step 4 — Diagnostic statistical tests

Step 5 — Baseline empirical modeling (Section 4: Main Results)

5.A Pattern A — Progressive controls (the canonical Table 2)

5.B Pattern B — Design horse race (Table 2-bis)

5.C Pattern C — Multi-outcome table (same X, several Y's)

5.D Pattern D — Stacked Panel A / Panel B table

5.E Pattern E — IV reporting triplet (first-stage / reduced-form / 2SLS)

5.F Pattern F — Causal-orchestrator main via csdid / synth_runner / teffects

5.G Pattern G — Subgroup esttab (Table 3, see Step 7)

5.H Pattern H — Robustness master (Table A1, see Step 6)

Canonical estimator commands (the underlying primitives)

Step 6 — Robustness battery

6.k Pattern H — Robustness master table (Table A1, one column per check)

6.l Specification curve (Simonsohn–Simmons–Nelson 2020) in Stata

6.m Sensitivity dashboard (HonestDiD + Oster + E-value, one block)

Step 7 — Further analysis (mechanism / heterogeneity / mediation / moderation)

Pre-registration `strategy.do` template

5.F Pattern F — Causal-orchestrator main via `csdid` / `synth_runner` / `teffects`

5.G Pattern G — Subgroup `esttab` (Table 3, see Step 7)