brycewang-stanford/Auto-Empirical-Research-Skills0°

Full-empirical-analysis-skill

仓库创建 2026年4月3日最近提交 21 天前SkillHot 收录 20 天前

⬈ GitHub 仓库 ⬈ SKILL.md 原文件 ⬇ Raw 原始文件

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灵活调用常用库，适合经典 Python 工具链的研究者。

▸ 风险提示

依赖多种 Python 包，需预装相应环境。

这个 Skill 做什么

基于传统 Python 生态的端到端经济学实证分析流水线。

用传统 Python 生态（pandas / numpy / scipy / statsmodels 等）把一篇计量经济学实证研究从数据清洗一路跑到图表和稳健性检验。适合写论文或做定量社会科学分析时调用标准化的 8 步流水线，会输出 AER/QJE/AEJ 风格的发表级别结果：多列回归表（M1→M6）、Table 1、机制/异质性/稳健性表，以及事件研究和系数趋势图。特点是每次运行都能产出可发表、可复现的完整结果集合，方便写作与复现。

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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 sh

研究检索经验分析Python计量工具链通用

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# 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

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