00.1 Full Empirical Analysis Skill Python
🤖 AI Summary
This skill executes a complete econometric analysis pipeline in Python, from data loading and cleaning through causal inference (DiD, RDD, IV) to publication-ready LaTeX tables and figures in AER/QJE style.
How to Install
Claude Code:
git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git && cp Auto-Empirical-Research-Skills/skills/00.1-Full-empirical-analysis-skill_Python ~/.claude/skills/SKILL.md -r--- 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-colum
Details
| Category | AI/ML → ml |
| Source | brycewang-stanford/Auto-Empirical-Research-Skills |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 2.3K |
| Est. per Skill | N/A (shared across 131 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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