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empirical-analysis-python

v1.0.0

Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml). Data cleaning → variable construction → Table 1 → diagnostics → estimation (OLS / IV / DID / RDD / PSM / SCM / DML / Causal Forest) → robustness battery → mechanism / heterogeneity / mediation → publication-ready tables & figures. Also covers epidemiology (TMLE / IPTW / Mendelian randomization / survival) and ML-causal (DML / meta-learners / Dragonnet) modes. Every line is explicit and swappable — built for teaching, referee-level audit, and strict replication.

Claude Code

By Bryce WangLicense: CC-BY-SA-4.04.4k GitHub starsUpdated 2 hours ago

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Claude Code
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Source updated
Sep 30, 2026
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Install empirical-analysis-python for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install empirical-analysis-python@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/empirical-analysis-python/.

Plugin files

plugins/empirical-analysis-python/
└── .claude-plugin/plugin.json

Plugin manifests1

plugins/empirical-analysis-python/.claude-plugin/plugin.json
{
  "name": "empirical-analysis-python",
  "description": "Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml). Data cleaning → variable construction → Table 1 → diagnostics → estimation (OLS / IV / DID / RDD / PSM / SCM / DML / Causal Forest) → robustness battery → mechanism / heterogeneity / mediation → publication-ready tables & figures. Also covers epidemiology (TMLE / IPTW / Mendelian randomization / survival) and ML-causal (DML / meta-learners / Dragonnet) modes. Every line is explicit and swappable — built for teaching, referee-level audit, and strict replication.",
  "version": "1.0.0",
  "author": {
    "name": "Bryce Wang",
    "email": "[email protected]"
  },
  "license": "CC-BY-SA-4.0",
  "homepage": "https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills",
  "repository": "https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills",
  "keywords": [
    "econometrics",
    "causal-inference",
    "python",
    "difference-in-differences",
    "instrumental-variables",
    "regression-discontinuity",
    "propensity-score-matching",
    "synthetic-control",
    "double-machine-learning",
    "empirical-research",
    "replication"
  ]
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

[empirical-analysis-python on Agent Plugins Marketplace](https://pluginsmp.com/plugins/empirical-analysis-python)