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home/skills/brycewang-stanford/auto-empirical-research-skills/00-full-empirical-analysis-skill_statspai
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00-Full-empirical-analysis-skill_StatsPAI

bybrycewang-stanford· 161 skills

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Data Science & Analytics

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TL;DR

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder sp.oaxaca, Kitagawa sp.kitagawa_decompose, DiNardo–Fortin–Lemieux sp.dfl_decompose, Gelbach sp.gelbach, Fairlie sp.fairlie, RIF / FFL sp.rif_decomposition, all reachable through the sp.decompose dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

How to install 00-Full-empirical-analysis-skill_StatsPAI?

brycewang-stanford/auto-empirical-research-skills/00-full-empirical-analysis-skill_statspai
$npx -y skills add brycewang-stanford/auto-empirical-research-skills --skill 00-Full-empirical-analysis-skill_StatsPAI

Installs into the current project.

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Use this skill

Run `npx skills use "https://github.com/brycewang-stanford/auto-empirical-research-skills" --skill "brycewang-stanford/auto-empirical-research-skills/00-full-empirical-analysis-skill_statspai"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.

Use the whole pack

Use the skills in "https://github.com/brycewang-stanford/auto-empirical-research-skills" that are relevant to the current task. Run `npx skills add "https://github.com/brycewang-stanford/auto-empirical-research-skills"` and select the relevant skills, then follow their instructions.

Preview

brycewang-stanford/auto-empirical-research-skillsbrycewang-stanford/auto-empirical-research-skills

$ npx -y skills add brycewang-stanford/auto-empirical-research-skills --skill 00-Full-empirical-analysis-skill_StatsPAI

▸ installing to .claude/skills…

✓ 00-Full-empirical-analysis-skill_StatsPAI ready

Repobrycewang-stanford/auto-empirical-research-skills
TypeSkills
CategoryData Science & Analytics
ForResearcherAnalyst
UpdatedJul 2026
License—
First seenJul 26, 2026

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