/00.1-Full-empirical-analysis-skill_Python

name: Full-empirical-analysis-skill
description: Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **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 pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
triggers:
  - full empirical analysis in Python
  - classical econometrics pipeline
  - traditional Python econometrics
  - end-to-end empirical workflow
  - pandas statsmodels linearmodels workflow
  - replicate an applied economics paper
  - data cleaning empirical
  - winsorize and standardize
  - variable construction
  - Table 1 summary statistics
  - balance table
  - correlation matrix
  - normality test
  - heteroskedasticity test
  - autocorrelation test
  - stationarity test
  - multicollinearity VIF
  - endogeneity test
  - baseline regression
  - panel fixed effects
  - DID workflow Python
  - event study
  - instrumental variables regression
  - regression discontinuity
  - propensity score matching
  - synthetic control python
  - double machine learning
  - robustness checks
  - placebo test
  - specification curve
  - alternative clustering
  - heterogeneity analysis
  - mechanism analysis
  - mediation analysis
  - moderation analysis
  - publication-ready regression table
  - coefplot
  - binscatter
  - event study plot
  # Mode A — Epidemiology / public health
  - epidemiology pipeline python
  - public health causal inference python
  - target trial emulation python
  - g-formula python
  - IPTW marginal structural model python
  - TMLE doubly robust python
  - HAL-TMLE python
  - Mendelian randomization python
  - MR-Egger weighted median python
  - STROBE TRIPOD reporting python
  - E-value sensitivity python
  - Kaplan-Meier AFT survival python
  - lifelines survival python
  - zepid epidemiology
  - 流行病学 python
  - 公共健康 python
  # Mode B — ML causal inference
  - ML causal inference python
  - double machine learning DML python
  - econml DoubleML
  - meta-learner S T X R DR python
  - causal forest GRF python
  - causalml meta-learner
  - Dragonnet TARNet CEVAE python
  - Bayesian causal forest BCF python
  - CATE distribution python
  - policy tree python
  - off-policy evaluation python
  - conformal causal prediction python
  - mapie conformal
  - fairness audit python
  - fairlearn audit
  - causal discovery PC NOTEARS python
  - causal-learn cdt
  - 因果机器学习 python

Full Empirical Analysis — Classical Python Workflow

This skill is the *canonical* 8-step pipeline an applied economist runs on every empirical paper, written in the **traditional Python ecosystem** — no opinionated one-stop wrapper. Every step calls libraries directly (`pandas`, `numpy`, `scipy`, `statsmodels`, `linearmodels`, `pyfixest`, `rdrobust`, `econml`, `causalml`, `matplotlib`, `seaborn`), so the agent — or the user reading the agent's code — has full visibility and can swap any component.

**Companion skill**: if the user prefers a single-import agent-native DSL (`import statspai as sp`), route to `00-StatsPAI_skill` instead. **This skill is the opposite philosophy**: everything explicit, everything inspectable, every diagnostic run by hand, every plot shaped by the user.

Philosophy