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README.md

Full Empirical Analysis Skill for Claude Code (Python)

This folder is a Claude Code Skill that teaches Claude (or any compatible agent harness) how to drive a complete empirical analysis end-to-end in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn — with explicit control at every step, covering three domain modes that share the same 8-step scaffolding:

  • Default — Applied Econ (AER / QJE / AEJ). The canonical 8-step pipeline: cleaning → variable construction → Table 1 → diagnostic tests → baseline modeling (OLS / pyfixest.feols HDFE / IV / DID / RD / SCM / matching / DML) → robustness gauntlet (placebo / Oster / honest_did / spec curve / alt-SE) → mechanism + heterogeneity → publication-ready Word / Excel / LaTeX bundle.
  • Mode A — Epidemiology / public health (§A). Target-trial emulation in pandas, IPTW + g-formula + TMLE doubly-robust triplet via zepid / econml, Mendelian randomization (IVW / Egger / weighted median) via pymr or rpy2+TwoSampleMR, KM / Cox / AFT / RMST survival via lifelines, E-value sensitivity, principal stratification — under STROBE / TRIPOD-AI reporting conventions.
  • Mode B — ML causal inference (§B). DML (econml.dml / doubleml), S/T/X/R/DR meta-learners (econml.metalearners / causalml), causal forest (econml.grf), Dragonnet / TARNet / CEVAE neural causal (causalml), BCF, matrix completion, CATE distribution + policy tree (econml.policy / policytree-py) + off-policy evaluation, conformal causal prediction (mapie), fairness audit (fairlearn), and DAG learning (causal-learn PC / cdt NOTEARS / LLM-assisted).

All three modes reuse the same Step 1–4 (cleaning → Table 1 → diagnostics) and Step 8 (publication tables / figures) scaffolding — switching modes only changes which Step-5 estimator family you reach for, not the surrounding paper structure.

Philosophy

This skill is the companion opposite of the 00-StatsPAI_skill in the same repository:

00-StatsPAI_skill 00.1-Full-empirical-analysis-skill
Import style import statspai as sp (one package) explicit, per-library imports
Abstraction agent-native high-level DSL raw, inspectable classical calls
Three modes Default Econ + §A Epi + §B ML causal Default Econ + §A Epi + §B ML causal (parallel)
Scope EDA → DSL → DAG → estimate → robust cleaning → transforms → descriptive → tests → model → robust → mechanism → tables
Best when you want a one-shot pipeline you want full control, or need to teach / audit each step

Both co-exist and ship the same three domain modes: use StatsPAI when you trust the DSL; use this skill when you want every line explicit and every library choice visible.

Install

# Option 1: copy
cp -r 00.1-Full-empirical-analysis-skill ~/.claude/skills/Full-empirical-analysis-skill

# Option 2: symlink (auto-follow upstream updates)
ln -s "$(pwd)/00.1-Full-empirical-analysis-skill" \
      ~/.claude/skills/Full-empirical-analysis-skill

Install the Python stack:

pip install pandas numpy scipy matplotlib seaborn \
            statsmodels linearmodels pyfixest \
            rdrobust rddensity \
            econml causalml \
            stargazer tableone missingno \
            pingouin arch binsreg
# Optional
pip install pyreadstat pysynth ebal

Activate

The skill auto-activates on natural-language triggers such as "run a full empirical analysis in Python", "replicate an applied economics paper", "build a Table 1 balance table", "winsorize at 1/99%", "run Breusch-Pagan", "placebo test", "event study plot", "mediation analysis", etc. Full list in the triggers: block of SKILL.md.

Scope

In scope — the canonical 8-step pipeline (v2 adds Step −1 / Step 0 / Step 2.5 / Step 3.5 sub-stages mirroring the StatsPAI reference skill):

Step −1 Pre-Analysis Plan         statsmodels.stats.power → pap.json
Step 0  Sample log + data contract sample_log + 5-check asserts → JSON
Step 1  Data cleaning              missing / outliers / dtype / join / panel
Step 2  Variable construction      log / winsorize / standardize / encode / lags
Step 2.5 Empirical strategy        equation × ID assumption × estimator → strategy.md
Step 3  Descriptive statistics     summary / Table 1 / correlation / distributions
Step 3.5 Identification graphics   event-study / 1st-stage F / McCrary / love / SCM trajectory
Step 4  Diagnostic tests           normality / hetero / autocorr / VIF / stationarity
Step 5  Baseline modeling          OLS / panel FE / IV / DID / RD / SC / matching / DML
        Patterns A–H               progressive ctrls / horse race / multi-Y / IV triplet / ...
Step 6  Robustness battery         placebo / subsample / spec curve / Oster δ*
        + Pattern H Master Table A1 + spec curve + sensitivity dashboard
Step 7  Further analysis           mechanism / heterogeneity / mediation / moderation
Step 8  Tables & figures           stargazer / coefplot / event study / forest plot
        + reproducibility stamp (artifacts/result.json)

Out of scope — paper drafting (LaTeX prose), DSGE / HANK numerical macro (see 20-wenddymacro-python-econ-skill), and opinionated agent-native DSLs (see 00-StatsPAI_skill). This skill hands you the polished tables and figures, not the written manuscript.

Files

  • SKILL.md — frontmatter + full agent playbook (8-step workflow + library cheat-sheet + common mistakes)
  • README.md — this file
  • references/ — deep per-step references, loaded on demand
    • 01-data-cleaning.md
    • 02-data-transformation.md
    • 03-descriptive-stats.md
    • 04-statistical-tests.md
    • 05-modeling.md
    • 06-robustness.md
    • 07-further-analysis.md
    • 08-tables-plots.md

When to read the references

SKILL.md alone covers the canonical call at each step. Reach for the matching references/NN-*.md file when:

  • The user's case doesn't fit the default (e.g. multi-cutoff RD → 05-modeling.md §6)
  • You need a variant-specific diagnostic (e.g. wild cluster bootstrap → 06-robustness.md §2)
  • You want the full table of alternatives (e.g. every normality test → 04-statistical-tests.md §1)

Each reference file is organized by section number so you can grep directly:

grep -n "^## " references/05-modeling.md

完整实证分析技能（中文，Python）

本文件夹是一份 Claude Code Skill，教 Claude（或任何兼容的 agent 运行时） 端到端地完成一次实证分析，使用的是传统 Python 计量生态 —— `pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml

  • causalml + matplotlib/seaborn`，每一步都显式可控。覆盖三种领域模式， 共用同一套 8 步骨架（清洗 / Table 1 / 诊断 / 出表）：
  • 默认 — 应用经济学（AER / QJE / AEJ）。8 步流程：清洗 → 变量构造 → Table 1 → 诊断检验 → 基准建模（OLS / pyfixest.feols HDFE / IV / DID / RD / SCM / 匹配 / DML）→ 稳健 gauntlet（安慰剂 / Oster / honest_did / 规范曲线 / 替代 SE）→ 机制 + 异质性 → 论文级 Word / Excel / LaTeX 三件套。
  • 模式 A — 流行病学 / 公共健康（§A）。pandas 写 target-trial 协议、 zepid / econml 跑 IPTW + g-formula + TMLE 双稳健三件套， pymr 或 rpy2+TwoSampleMR 做孟德尔随机化（IVW / Egger / 加权中位数）， lifelines 做 KM / Cox / AFT / RMST 生存分析，E-value 敏感性， principal stratification——按 STROBE / TRIPOD-AI 报告规范输出。
  • 模式 B — 因果机器学习（§B）。DML（econml.dml / doubleml）， S/T/X/R/DR meta-learner（econml.metalearners / causalml）， causal forest（econml.grf），Dragonnet / TARNet / CEVAE 神经因果 （causalml），BCF，matrix completion，CATE 分布 + policy tree （econml.policy / policytree-py）+ off-policy 评估，conformal causal 预测区间（mapie），fairness audit（fairlearn），DAG 学习 （causal-learn PC / cdt NOTEARS / LLM 辅助）。

三种模式共用同一套 Step 1–4（清洗 / Table 1 / 诊断）和 Step 8（出表 / 出图） 骨架——切换模式只换 Step-5 估计器组合，前后骨架保持一致。

哲学

本 skill 与同仓库中的 00-StatsPAI_skill 互为补位：

00-StatsPAI_skill 00.1-Full-empirical-analysis-skill
引入风格 import statspai as sp 各库显式引入
抽象层次 面向 agent 的高层 DSL 原始、可审计的经典调用
三种模式 默认 Econ + §A 流行病 + §B ML 因果 默认 Econ + §A 流行病 + §B ML 因果（一一对应）
覆盖步骤 EDA → DSL → DAG → 估计 → 稳健 清洗 → 变量构造 → 描述 → 检验 → 建模 → 稳健 → 机制 → 出表
适用场景 一键跑完整 pipeline 需要全量控制、审计每一步、或做教学

两者并行存在，且三种领域模式一一对应：信任 DSL 时用 StatsPAI；要逐行审计、 展示每个库选择时用本 skill。

安装

# 方式 1：复制
cp -r 00.1-Full-empirical-analysis-skill ~/.claude/skills/Full-empirical-analysis-skill

# 方式 2：软链
ln -s "$(pwd)/00.1-Full-empirical-analysis-skill" \
      ~/.claude/skills/Full-empirical-analysis-skill

激活

触发词包括 "在 Python 里跑一次完整实证分析"、"复现一篇应用经济学文章"、 "做一个 Table 1 平衡性表"、"1/99 缩尾"、"Breusch-Pagan 检验"、 "安慰剂检验"、"事件研究图"、"中介分析" 等。完整列表见 SKILL.md frontmatter 中的 triggers:。

覆盖范围

覆盖 —— 经典 8 步 pipeline（v2 对齐 StatsPAI 参考 skill，新增 Step −1 / Step 0 / Step 2.5 / Step 3.5 子阶段）：

Step −1 预分析计划（PAP）  statsmodels.stats.power → pap.json
Step 0  样本日志 + 数据契约 sample_log + 5 项 assert → JSON
Step 1  数据清洗            缺失 / 异常 / dtype / 合并 / 面板
Step 2  变量构造            log / 缩尾 / 标准化 / 编码 / 滞后
Step 2.5 实证策略           方程式 × 识别假设 × 估计器 → strategy.md
Step 3  描述统计            summary / Table 1 / 相关 / 分布
Step 3.5 识别图            事件研究 / 一阶段 F / McCrary / love / SCM 轨迹
Step 4  诊断检验            正态 / 异方差 / 自相关 / VIF / 平稳
Step 5  基准建模            OLS / 面板 FE / IV / DID / RD / SC / 匹配 / DML
        八种 regtable 模式 A–H  渐进控制 / 设计赛马 / 多 Y / IV 三联 / ...
Step 6  稳健性检验          安慰剂 / 子样本 / 规范曲线 / Oster δ*
        + Pattern H 稳健性主表 A1 + 规范曲线 + 敏感性面板
Step 7  进一步分析          机制 / 异质性 / 中介 / 调节
Step 8  表与图              stargazer / 系数图 / 事件研究图 / 森林图
        + 复现戳（artifacts/result.json）

不覆盖 —— 正文撰写（LaTeX 文字）、DSGE / HANK 数值宏观 （见 20-wenddymacro-python-econ-skill）、以及 agent-native DSL （见 00-StatsPAI_skill）。本 skill 交付的是打磨好的表和图，不包括正文。

文件

  • SKILL.md — frontmatter + 完整 agent 手册（8 步流程 + 选库表 + 常见坑）
  • README.md — 本文件
  • references/ — 每步的深层参考，按需加载
    • 01-data-cleaning.md
    • 02-data-transformation.md
    • 03-descriptive-stats.md
    • 04-statistical-tests.md
    • 05-modeling.md
    • 06-robustness.md
    • 07-further-analysis.md
    • 08-tables-plots.md