--- name: Stata Empirical Analysis slug: stata-empirical-analysis category: Data description: Stata Empirical Analysis provides a complete 8-step pipeline for econometric and causal inference in Stata, covering data cleaning, variable construction, diagnostics, estimation (DID, IV, RD, matching), robustness checks, and publication-ready tables and figures. Use it when you need reproducible .do-file workflows for academic empirical papers. github: "https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/00.2-Full-empirical-analysis-skill_Stata" language: Stata stars: 3419 forks: 445 install: "npx degit https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/00.2-Full-empirical-analysis-skill_Stata ~/.claude/skills/00.2-Full-empirical-analysis-skill_Stata" installs_to: ~/.claude/skills/00.2-Full-empirical-analysis-skill_Stata source_path: skills/00.2-Full-empirical-analysis-skill_Stata/SKILL.md collection_size: 25 category_size: 668 collection_url: "https://dirskills.com/collections/brycewang-stanford/Auto-Empirical-Research-Skills" added: 2026-08-16T07:02:46.675Z last_synced: 2026-08-16T07:02:46.675Z canonical_url: "https://dirskills.com/skills/stata-empirical-analysis" --- # Stata Empirical Analysis Stata Empirical Analysis provides a complete 8-step pipeline for econometric and causal inference in Stata, covering data cleaning, variable construction, diagnostics, estimation (DID, IV, RD, matching), robustness checks, and publication-ready tables and figures. Use it when you need reproducible .do-file workflows for academic empirical papers. **Install:** ```bash npx degit https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/00.2-Full-empirical-analysis-skill_Stata ~/.claude/skills/00.2-Full-empirical-analysis-skill_Stata ``` ## README # 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/`](references/) holds variant-specific depth (dozens of tests, estimator-specific diagnostics, graph recipes). ## SkillOpt-style execution gate Use this long playbook as a seed skill, not as a script to exhaustively apply. SkillOpt discipline: treat each local `.do`-file change as a candidate patch that must beat a selection check and survive a held-out check before it becomes reusable boilerplate. Before writing or revising a `.do` file, compress the user's request into a task-local `best_skill` card: ```text best_skill: train_signal: selection_split: heldout_gate: accepted_patterns: rejected_patterns: patch_scope: reject_if: ``` 1. **Route card**: record the mode (`econ`, `epi`, or `ml-causal`), estimand, identification design, focal outcome/treatment, Stata command family, and required artifacts. 2. **Bounded edit**: change one decision at a time (sample rule, estimator command, fixed effects, clustering, export format, or robustness check). Prefer the smallest patch that can pass validation. 3. **Selection split discipline**: treat the user's immediate failure or requested artifact as the selection split. Reserve at least one alternate outcome, sample window, estimator command family, or export target as the held-out gate. 4. **Held-out gate**: define checks before running code: `isid`/duplicate status, merge assertions, treatment support, missingness thresholds, expected table/figure files, and one non-focal robustness/specification that the change must not break. 5. **Reject buffer**: if a candidate spec fails the gate, log the failure, `.do` diff, and gate output in `analysis_log.md` or the Stata log; revert to the last passing spec and do not retry the same unchecked pattern. 6. **Slow/meta update**: at the end of the task, write down `accepted_patterns` and `rejected_patterns` from the trajectory. Do not widen the canonical `.do` template from a single passing run. 7. **Promote only after validation**: only turn a one-off fix into reusable `.do` boilerplate after it passes the current data and at least one alternate outcome/sample/specification. --- ## 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: | 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 | **How to invoke a non-default mode** (Claude / agent picks this up from the user's wording): | 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 | The three modes share **the same Step 1–4 cleaning / Table 1 / diagnostics scaffolding, the same Step 8 export stack, and the same DAG-first identification logic** — switching modes only changes which Step-5 command family you reach for. If you only want descriptive stats / Table 1 / a balance check, the AER `tabstat` / `balancetable` / `asdoc` calls in Step 3 work identically across all three modes. > **Stata-specific caveat for Mode B**: Stata's first-party ML-causal coverage is thinner than Python/R. For Dragonnet / TARNet / CEVAE / cfcausal / fairness audit, call out to Python via Stata 18's `python:` / `python script` block (or shell out to a sister `.py`) and read the result back via `frame` or `import delimited`. The skill prefers native Stata commands (`ddml`, `pdslasso`, `crforest`) where they exist, and explicitly marks the Python callouts in §B. --- ## Default Output Spec — Economics Empirical Paper This skill defaults to the **applied-economics paper convention**. Unless the user explicitly asks for a single point estimate, every `.do`-file run produces the full publication-ready output set below. Treat it as the contract of Step 8 — **mandatory**, not opt-in. ### Required tables (always produced) | # | 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}` | > **★ Table 2 is the centerpiece of every economics paper.** It is the multi-column regression table that walks the reader from raw correlation (M1) to the fully-specified design (M6: 2-way FE + interacted FE + cluster-robust SE). Do **not** collapse it into a single column. Do **not** report only the headline coefficient. The progression *is* the credibility argument: if M1→M6 is monotone and stable, the design is plausibly identifying; if it collapses on adding FE, that *is* the result. > > **Canonical 6 columns, in order:** > 1. **M1** raw bivariate (`reg y treat`) > 2. **M2** + demographics (`+ age + edu`) > 3. **M3** + sector controls (`+ tenure / firm_size`) > 4. **M4** + unit FE (`reghdfe ..., absorb(unit)`) > 5. **M5** + 2-way FE (`absorb(unit year)`) > 6. **M6** + interacted FE (`absorb(unit year i.industry#i.year)`) with `vce(cluster unit)` ### Required figures (always produced) | # | 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` | ### Output file layout (default) ``` project/ ├── tables/ table1_balance.{tex,rtf,xlsx,docx} table2_main.{tex,rtf,xlsx,docx} │ table3_mechanism.{tex,rtf,xlsx,docx} table4_heterogeneity.{tex,rtf,xlsx,docx} │ table5_robustness.{tex,rtf,xlsx,docx} └── figures/ fig1_trend.{pdf,png} fig2_event_study.{pdf,png} fig3_coefplot.{pdf,png} fig4_sensitivity.{pdf,png} ``` Every table → `.tex` (LaTeX `booktabs`) **and** `.rtf` (Word) **and** `.xlsx` (Excel) **and** `.docx` (Word OOXML). Every figure → `.pdf` (vector for LaTeX) **and** `.png` at ≥300 dpi (slides / web). ### When to deviate - **Single quick estimate** — produce only the relevant cell, but warn that the standard deliverable is the full set above and offer to run it. - **Design does not support a figure** (cross-section → no event study) — skip with a printed `display` note explaining why; do **not** silently drop. - **N=1 treated unit (`synth`)** — replace F1/F2 with the SCM trajectory + placebo distribution from `synth_runner`; T1–T5 still apply. --- ## Required packages ```stata * 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 ``` --- ## The 8 Steps — Canonical Pipeline (mapped to AER paper sections) ``` ┌──────────────────────────────────────────────────────────────────────┐ │ Step −1 Pre-Analysis Plan (PAP) power/sampsi/clustersampsi/MDE │ │ Step 0 Sample log + data contract sample_log/assert/xtdescribe/JSON │ │ Step 1 Data import & cleaning use/import/destring/misstable/merge│ │ Step 2 Variable construction gen/egen/winsor2/xtile/xtset/L.F.D.│ │ Step 2.5 Empirical strategy equation × ID assumption + pre-reg │ │ Step 3 Descriptive statistics tabstat/balancetable/asdoc/pwcorr │ │ Step 3.5 Identification graphics event-study/1st-stage/McCrary/love │ │ Step 4 Diagnostic tests sktest/hettest/xtserial/vif/dfuller│ │ Step 5 Baseline modeling reghdfe/ivreg2/csdid/rdrobust/synth│ │ Step 6 Robustness battery bacondecomp/honestdid/rwolf/boottest│ │ Step 7 Further analysis triple-diff/subgroup/medsem/margins│ │ Step 8 Tables & figures esttab/outreg2/coefplot/rdplot │ └──────────────────────────────────────────────────────────────────────┘ ``` The 8 steps mirror the canonical sections of an applied AER / QJE / AEJ paper. Each step is one paper section and emits a paper-ready artifact on disk: ``` Paper section Step Stata moves ─────────────────────────── ───── ──────────────────────────────────────────────── Pre-Analysis Plan −1 power/sampsi + freeze protocol.do to disk §1. Data 0 sample_log + 5-check data contract → JSON §1. Data 1 use/import/destring/misstable/merge assert/xtset §1. Data 2 gen/egen/winsor2/xtile/L./F./D./CPI deflation §1.1 Descriptives (Table 1) 3 tabstat · balancetable · asdoc · pwcorr · twoway §2. Empirical Strategy 2.5 write equation + ID assumption → strategy.do §3. Identification graphics 3.5 event-study · 1st-stage F · McCrary · love · SCM §3.5 Diagnostics 4 swilk · hettest · xtserial · vif · dfuller · hausman §4. Main Results (Table 2) 5 M1→M6 progressive controls + FE (eststo + esttab) §5. Heterogeneity (Table 3) 7 margins/marginsplot · subgroup · medsem §6. Mechanisms / Channels 7 medsem/khb · outcome ladder · DDD interactions §7. Robustness gauntlet 6 bacondecomp · honestdid · psacalc · boottest · ritest · rwolf §8. Replication package 8 esttab + outreg2 + coefplot + reproducibility stamp ``` Below is the canonical command at each step. **All examples share one running narrative** — a labor-economics panel where `training` (treatment) affects `log_wage` (outcome), with covariates `age`, `edu`, `tenure`, panel keys `worker_id` / `firm_id` / `year`. Variable names and parameter values are **illustrative**; substitute the real ones from the user's dataset. Only command names and option *shapes* are normative. > **When a step has many variants** (e.g. staggered DID has 5 estimators; heteroskedasticity has 4 classic tests), SKILL.md shows the one you reach for first and links to `references/NN-.md` for the rest. **Read the reference file when the user's case doesn't fit the default.** --- ## Paper-ready figure & table inventory (what to produce by section) A modern AER paper has **5–7 figures** and **3–5 main tables** + an appendix robustness table. Every step below leaves at least one numbered artifact on disk. Default file names assume parallel `.tex` / `.rtf` exports (the agent should produce both so co-authors can edit in Word, and the build system can use LaTeX): | § | 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}` | > Every Stata estimator above stores results via `eststo` and can be passed straight into `esttab`