Full Empirical Analysis Skill
Classical end-to-end empirical analysis workflow in the traditional Python econometric...
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill Full-empirical-analysis-skill -g -a claude-code -y- Repository
- brycewang-stanford/Auto-Empirical-Research-Skills4.9k
- Category
- Testing
- Updated
- 5d ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
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` /
Full Empirical Analysis, Classical Python Workflow
This skill is the canonical 8-step pipeline an applied economist runs on every empirical paper, written in the traditional Python ecosystem, no opinionated one-stop wrapper. Every step calls libraries directly (pandas, numpy, scipy, statsmodels, linearmodels, pyfixest, rdrobust, econml, causalml, matplotlib, seaborn), so the agent, or the user reading the agent's code, has full visibility and can swap any component.
Companion skill: if the user prefers a single-import agent-native DSL (import statspai as sp), route to 00-StatsPAI_skill instead. This skill is the opposite philosophy: everything explicit, everything inspectable, every diagnostic run by hand, every plot shaped by the user.
Philosophy
- Traditional stack, no magic. Agents should be able to read every line and know exactly which library / estimator / standard error family is at work.
- Full pipeline, not just estimation. 80% of the time on a real paper is steps 1, 4 and 6, 8. This skill treats them as first-class, not an afterthought.
- Rich outputs. Every step produces at least one table or figure, never a single point estimate in isolation.
- Progressive disclosure. SKILL.md gives the canonical call at each step;
references/holds variant-specific depth (dozens of tests, estimator-specific diagnostics, plot recipes). - Reproducible. Every code block is runnable after
pip install -r requirements.txtanddf = pd.read_csv(...).
SkillOpt-style execution gate
Use this long playbook as a seed skill, not as a script to exhaustively apply. SkillOpt discipline: treat each local analysis-code change as a candidate patch that must beat a selection check and survive a held-out check before it becomes reusable boilerplate. Before writing or revising analysis code, compress the user's request into a task-local best_skill card:
