/00-Full-empirical-analysis-skill_StatsPAI

name: StatsPAI_skill
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 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".
triggers:
  - causal inference in python
  - applied microeconomics pipeline
  - AER empirical analysis
  - QJE style robustness
  - DID IV RD SCM
  - callaway_santanna
  - synthetic control
  - double machine learning
  - causal forest
  - event study plot
  - first stage F-statistic
  - Oster bound
  - honest_did
  - spec_curve
  - estimand-first DSL
  - LLM-assisted DAG discovery
  - Oaxaca-Blinder decomposition
  - Kitagawa decomposition
  - DiNardo-Fortin-Lemieux decomposition
  - Gelbach decomposition
  - RIF regression decomposition
  - wage gap decomposition
  - text as treatment
  - export regression table to Word
  - export regression table to Excel
  - regression table docx
  - regression table xlsx
  - outreg2 in Python
  - summary_col equivalent
  - modelsummary equivalent
  - AER house style table
  - QJE house style table
  - journal template regression
  - Stata collect equivalent
  - replication bundle
  - sp.regtable
  - sp.collect
  - sp.paper_tables
  - sp.feols
  - sp.cite
  - high-dim fixed effects
  - two-way clustering
  - StatsPAI
  - statspai
  - fmt auto regression table
  - magnitude-adaptive coefficient formatting
  - mixed magnitude coefficients
  - sumstats by_labels
  - Control Treated auto labels
  - epidemiology pipeline
  - public health causal inference
  - target trial emulation
  - g-formula
  - IPTW marginal structural model
  - TMLE doubly robust
  - HAL-TMLE
  - Mendelian randomization
  - MR-Egger weighted median
  - STROBE TRIPOD reporting
  - E-value sensitivity
  - Kaplan-Meier AFT survival
  - 流行病学
  - 公共健康
  - ML causal inference
  - double machine learning DML
  - meta-learner S T X R DR
  - causal forest GRF
  - Dragonnet TARNet CEVAE
  - Bayesian causal forest BCF
  - CATE distribution
  - policy tree
  - off-policy evaluation
  - conformal causal prediction
  - fairness audit
  - causal discovery PC NOTEARS
  - 因果机器学习

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**: JOSS submission under review; JSS materials in `Paper-JSS/README.md` and `docs/jss_source_audit_dossier.md`

> **Install the right extras or the documented calls will raise `ImportError`.** Several core functions live behind optional dependency groups (verified from `pyproject.toml`): > > | You use… | Needs extra | Install | Symptom if missing | > |---|---|---|---| > | `sp.feols` / `sp.fepois` / `sp.feglm` (high-dim FE — the **default** for any `y ~ x \| fe` regression) | `fixest` (pyfixest) | `pip install "statspai[fixest]"` | `ImportError: pyfixest is required …` | > | Any figure (`sp.coefplot`, `sp.binscatter`, event-study/RD/SCM plots, `.plot()`) | `plotting` (matplotlib/seaborn) | `pip install "statspai[plotting]"` | `ImportError` on first plot | > | `sp.dragonnet` / `sp.tarnet` / `sp.cfrnet` / `sp.cevae` (neural causal) | `neural` (torch) | `pip install "statspai[neural]"` | `ImportError: PyTorch is required …` | > | `sp.causal_text.*` (te