Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
|---|---|
| 2026-07-31 | 288 |
| 2026-08-01 | 288 |
| 2026-08-06 | 288 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
15.0
growth rate 0.00%/day
[English](https://github.com/brycewang-stanford/statspai/blob/main/README.md) | [中文](https://github.com/brycewang-stanford/statspai/blob/main/README_CN.md) <p align="center"> <img src="https://raw.githubusercontent.com/brycewang-stanford/StatsPAI/main/docs/logo/readme-1.png" alt="StatsPAI - Python-native Stata and R replacement for applied causal inference" width="780"> </p> # 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: `regress`, `ivregress`, `reghdfe`, `csdid`, `rdrobust`, `synth`, `psmatch2`, `outreg2`. - R-style routines: `lm`, `fixest`, `did`, `rdrobust`, `Synth`, `DoubleML`, `MatchIt`, `modelsummary`, `broom`. - Python-native outputs: `.summary()`, `.tidy()`, `.plot()`, `.to_latex()`, `.to_docx()`, `.to_agent_summary()` where supported by the result object. - Companion Stata tooling: our own [`stata-code`](https://github.com/brycewang-stanford/stata-code/) can work with StatsPAI so agents can understand existing Stata workflows, translate them into Python, and cross-check results more smoothly. - Companion skill repos: [`Auto-Empirical-Research-Skills`](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills), [`AER-Skills`](https://github.com/brycewang-stanford/AER-Skills), [`Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills), and [`Paper-WorkFlow`](https://github.com/brycewang-stanford/Paper-WorkFlow) can work alongside StatsPAI and an agent as the methods, journal, manuscript, and reproducibility skill layer. StatsPAI is not a promise that every Stata/R command is bit-for-bit identical. When exact external parity matters, use the `validation_status` metadata, the reference-parity tests, and `sp.cross_validate` to see what has been certified for that estimator. `validation_status` distinguishes certified/validated evidence from API-stable breadth: a `certified` or `validated` symbol carries numerical evidence, while `api_stable` denotes a stable interface without a numerical-parity claim. --- ## Install ```bash pip install statspai ``` Then: ```python import statspai as sp print(sp.datasets.list_datasets()[["name", "design", "n_obs"]].head()) ``` StatsPAI ships teaching datasets such as Card (1995), Callaway-Sant'Anna `mpdta`, Lee (2008) RD, LaLonde/NSW, and California Proposition 99. The examples below run offline after installation. At a glance: 1,154 registered functions across 87 submodules; 352k
Excerpt of 17,663 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ff0e5abdb5b336c0, topic:mcp, desc:mcp server
matched fp:ff0e5abdb5b336c0, topic:llm
matched fp:ff0e5abdb5b336c0, topic:ai-agents