Claude Marketplaces
brycewang-stanford avatar

empirical-analysis-python

brycewang-stanford/Auto-Empirical-Research-Skills

Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml): cleaning -> variable construction -> Table 1 -> diagnostics -> estimation (OLS/IV/DID/RDD/PSM/SCM/DML/Causal Forest) -> robustness -> mechanism/heterogeneity/mediation -> publication-ready tables & figures. Includes epidemiology and ML-causal modes.

version
1.0.0
GitHub stars
4.8K
View on GitHubHomepage

Install

/plugin install empirical-analysis-python@auto-empirical-research-skills

Run it inside Claude Code after adding the marketplace with /plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills.

Contents

Discovered from source

Skills 1

Sponsors

SnugThe clipboard manager you and your agent can use, safelyAgent CaffeineClose your lid, come back to finished workWaxBlock anything with supervision, without erasing your iPhoneOpen StepsSponsoredFree and open-source skills that make AI coding agents easier to understand, verify, and control.

Details

Marketplace
brycewang-stanford/Auto-Empirical-Research-Skills
Source
plugins/empirical-analysis-python
Author
Bryce Wang
Category
academic
License
CC-BY-SA-4.0
Keywords
econometrics, causal-inference, python, difference-in-differences, instrumental-variables, regression-discontinuity, double-machine-learning, empirical-research, replication

More from brycewang-stanford/Auto-Empirical-Research-Skills

All 4