StatsPAI

StatsPAI is a Data skill for Claude Code, published by brycewang-stanford in Auto-Empirical-Research-Skills.

3.4K stars445 forkson brycewang-stanford/Auto-Empirical-Research-SkillsAdded 2026/08/16+1% in starsRepository updated 2026/08/10

View source on GitHub Download repo ZIP

academic-researchagent-skillsai-agentawesome-listcommunicationcopapereconomicseducationempirical-researchinternational-relationspolitical-sciencepsychologypublic-administrationreproducible-researchskills-librarysocial-sciencesociology

Install in seconds

Install StatsPAI

Copy StatsPAI into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/00-Full-empirical-analysis-skill_StatsPAI ~/.claude/skills/00-Full-empirical-analysis-skill_StatsPAI

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/00-Full-empirical-analysis-skill_StatsPAI/SKILL.md in brycewang-stanford/Auto-Empirical-Research-Skills
Installs to: ~/.claude/skills/00-Full-empirical-analysis-skill_StatsPAI
Collection: One of 25 skills cataloged from this repository
Category: Data — 668 skills

What StatsPAI does

StatsPAI runs full empirical and causal analysis pipelines in Python, including AER-style DID/RD/IV/SCM/DML tables, epidemiological target-trial emulation, ML causal inference, and decomposition methods. Use it when the user asks for applied micro, public health, or causal ML results with paper-ready Word/Excel/LaTeX outputs.

StatsPAI is cataloged under Data on DirSkills. StatsPAI comes from a repository tagged academic-research, agent-skills, ai-agent, awesome-list and communication.

Documentation

README

Excerpt

StatsPAI: Agent-Native Causal Inference & AER-Style Empirical Workflow

StatsPAI is a validation-tiered Python package for causal inference and applied econometrics: one import statspai as sp, 1,100+ registered functions behind a self-describing API, and mature estimator result objects that commonly export to LaTeX / Word / Excel / BibTeX.

This skill drives StatsPAI through the canonical pipeline of an applied AER empirical paper. Each step emits a paper-ready artifact (Table 1, event-study figure, Table 2 main results, robustness panel, replication stamp).

This is the opening of the README. Read the full README on GitHub.

Frequently asked about StatsPAI

What else does brycewang-stanford publish alongside StatsPAI?
StatsPAI is one of 25 skills that DirSkills catalogs from brycewang-stanford/Auto-Empirical-Research-Skills, the repository it ships in. Its siblings there include Academic Paper Composer, Academic Proofreader and Auto-Empirical Research. Each one is a separate skill with its own page in this directory, installs the same way StatsPAI does, and is maintained by brycewang-stanford in that same repository. The rest of the collection is listed on the brycewang-stanford/Auto-Empirical-Research-Skills page.
How does StatsPAI compare to other Data skills?
StatsPAI ranks #208 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of StatsPAI against them. Open each page to compare what they document and how they install.