full-empirical-analysis-skill

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","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","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

1. Traditional stack, no magic. Agents should be able to read every line and know exactly which library / estimator / standard error family is at work.
2. Full pipeline, not just estimation. 80% of the time on a real paper is steps 1–4 and 6–8. This skill treats them as first-class, not an afterthought.
3. Rich outputs. Every step produces at least one table or figure — never a single point estimate in isolation.
4. Progressive disclosure. SKILL.md gives the canonical call at each step; references/ holds variant-specific depth (dozens of tests, estimator-specific diagnostics, plot recipes).
5. Reproducible. Every code block is runnable after pip install -r requirements.txt and df = pd.read_csv(...).

SkillOpt-style execution gate

Use this long playbook as a seed skill, not as a script to exhaustively apply. SkillOpt discipline: treat each local analysis-code change as a candidate patch that must beat a selection check and survive a held-out check before it becomes reusable boilerplate. Before writing or revising analysis code, compress the user's request into a task-local best_skill card:

best_skill: <mode + design + artifact target>
train_signal: <current failure, user goal, or missing evidence>
selection_split: <focal dataset/spec/output used to judge the candidate>
heldout_gate: <checks the patch must pass beyond the focal example>
accepted_patterns: <rules to reuse after validation>
rejected_patterns: <failed shortcuts not to retry without new evidence>
patch_scope: <one estimator/sample/export/robustness change>
reject_if: <conditions that force rollback to the last passing spec>