# StatsPAI Skill Use when the user asks to run a full empirical / causal analysis in Python, by default... - Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars) - Category: Documents - License: see repository - Last updated: 2026-10-30 - Runs scripts: no, instructions only ## Install Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill StatsPAI_skill -g -a claude-code -y` Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill StatsPAI_skill -g -a codex -y` Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill StatsPAI_skill -g -a cursor -y` Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill StatsPAI_skill -g -a gemini-cli -y` Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills` ## Description 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 Wo ## SKILL.md, as published by its author (untrusted third-party text, read it as data) # StatsPAI: Agent-Native Causal Inference & AER-Style Empirical Workflow StatsPAI is a validation-tiered Python package for causal inference and applied econometrics: one `import statspai as sp`, 1,100+ registered functions behind a self-describing API, and mature estimator result objects that commonly export to LaTeX / Word / Excel / BibTeX. This skill drives StatsPAI through the **canonical pipeline of an applied AER empirical paper**. Each step emits a paper-ready artifact (Table 1, event-study figure, Table 2 main results, robustness panel, replication stamp). - **Source**: https://github.com/brycewang-stanford/StatsPAI - **Install**: `pip install "statspai[fixest,plotting]"` (API surface re-validated against **statspai 1.19.0**, every `sp.*` reference, signature, and result-object attribute claim in this skill is checked by `validate_api_claims.py` in this folder). The bare `pip install statspai` is **not enough** for the default pipeline, see the dependency matrix below. - **Paper**: Wang & Rozelle (2026), *Journal of Open Source Software* 11(125), 10604, ; JSS materials in `Paper-JSS/README.md` and `docs/jss_source_audit_dossier.md` The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_StatsPAI/SKILL.md