Full-empirical-analysis-skill

Full-empirical-analysis-skill is a Statistical modelling skill for AI agents, published by brycewang-stanford in the Auto-Empirical-Research-Skills repository on GitHub. Skillspace rates Full-empirical-analysis-skill D (56/100) and it passed the safety scan; the rating is based on static analysis only.

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…

Grade
D
Quality
56 / 100
Safety
Passed
GitHub stars
3,783

Unproven: rated by static analysis only, not yet compared head-to-head.

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View source on GitHubbrycewang-stanford/Auto-Empirical-Research-Skills

Our review

Architecture
66 / 100
Maturity
Structured: ships references or scripts
Security scan
No issues found
License
Not declared. Check the repository before reusing it.

Not checked yet

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