StatsPAI
Description
StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills, an MCP server, and R/Stata parity validation.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
[English](https://github.com/brycewang-stanford/statspai/blob/main/README.md) | [中文](https://github.com/brycewang-stanford/statspai/blob/main/README_CN.md)
StatsPAI: an Agent&Python-native Stata/R replacement for applied causal inference
[](https://pypi.org/project/StatsPAI/) [](https://pypi.org/project/StatsPAI/) [](https://github.com/brycewang-stanford/statspai/blob/main/LICENSE) [](https://github.com/brycewang-stanford/statspai/actions) [](https://brycewang-stanford.github.io/StatsPAI/) [](https://pepy.tech/projects/statspai) [](https://joss.theoj.org/papers/9f1c837b1b1df7adfcdd538c3698e332) [](https://doi.org/10.5281/zenodo.19933900)
StatsPAI is for empirical researchers who would normally jump between Stata, R, and Python. Its goal is to make common Stata/R econometrics and causal-inference workflows feel native in Python: load a dataset, estimate a model, inspect diagnostics, export tables, and hand the result to an agent or notebook without leaving one API.
It is meant to be a practical replacement path for new Python-first work:
- Stata-style routines: `regres
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