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00 Full Empirical Analysis Skill Statspai — ★ 2.3K GitHub Stars — Install Guide | SkillsNav
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00 Full Empirical Analysis Skill Statspai

★ 2.3K repomlN/AIntermediateClaude
🤖 AI Summary

This AI agent skill automatically generates a complete empirical analysis pipeline in Python, including model specification (DID, RD, IV, SCM, DML, matching), written identifying assumptions, and a full output suite (Table 1, Table 2, event-study plots, robustness checks) in the style of top economics journals or epidemiology/public health.

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-Full-empirical-analysis-skill_StatsPAI ~/.claude/skills/SKILL.md -r
--- name: StatsPAI_skill description: Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health

Details

Category AI/ML → ml
Sourcebrycewang-stanford/Auto-Empirical-Research-Skills
SKILL.mdView on GitHub →
Repo Stars★ 2.3K
Est. per SkillN/A (shared across 131 skills from this repo)
DifficultyIntermediate
Risk LevelN/A

Related Skills

Works Well With

Skills from the same repository — often designed to work together