ZBS Index What actually exists in applied AI, with the source next to it

skill

Full-empirical-analysis-skill

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 share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

Description as published by the maintainer. Source

  • active

active — Most recent push to the repository was 2026-08-06.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Signal Value What it measures Window Observed Source
GitHub stars 3,289 Stars on the repository that contains this skill, not on the skill itself. A collection of fifty skills shares one number, so it says nothing about this particular skill. cumulative, all time GitHub
Last commit 2026-08-06 Most recent push to the containing repository. It may reflect work on a different skill in the same collection. point in time GitHub
repository status active The repository holding this skill exists and is not archived. as of fetch GitHub

Will this work with your setup?

No harness stated by the author and no install path convention detected. Compatibility is untested.

We have not run this skill against a task with and without it enabled, so we cannot tell you whether it improves anything, what it costs in tokens, or whether it duplicates behaviour your harness already has. When we have run that test, the result will appear on this page with the task, the versions and the budget it used.

The skill definition lives at plugins/empirical-analysis-python/skills/pipeline/SKILL.md in https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.

Where to get it

Related, by what their authors tagged them

These share tags the maintainers applied themselves, such as academic-research, awesome-list, communication, copaper. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.

This is not a recommendation and not a test result. It is a map of what the authors said their work is about.

Also from brycewang-stanford

How the author describes it

Topics the maintainer set on GitHub: academic-research, agent-skills, ai-agent, awesome-list, communication, copaper, economics, education, empirical-research, international-relations, political-science, psychology, public-administration, reproducible-research, skills-library, social-science, sociology.

This record as data

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Sources

  1. brycewang-stanford/Auto-Empirical-Research-Skills on GitHub — GitHub, observed , trust tier 3.