00.1-Full-empirical-analysis-skill_Python

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".