Is the StatsPAI MCP server actively maintained?

The StatsPAI MCP server was last committed 29d ago and has 6 contributors. Issue health: 100% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

StatsPAI MCP Server

Name: brycewang-stanford/StatsPAI
Rating: 4.7 (1 reviews)
Author: brycewang-stanford

brycewang-stanford/StatsPAI

Score: 93.2 → 0% Rank #158 Agent Framework

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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.

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Overview

brycewang-stanford/StatsPAI is a Python MCP server licensed under MIT. 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. Topics: agent-native, ai-agents, causal-discovery, causal-inference, data-science, difference-in-differences, double-machine-learning, econometrics, instrumental-variables, llm, mcp, panel-data, policy-evaluation, python, regression-discontinuity, rstats, stata, statistics, synthetic-control, treatment-effects.

Ranked #158 out of 24846 indexed tools.

In the top 1% of all indexed tools.

Ecosystem

Python MIT

agent-nativeai-agentscausal-discoverycausal-inferencedata-sciencedifference-in-differencesdouble-machine-learningeconometricsinstrumental-variablesllmmcppanel-datapolicy-evaluationpythonregression-discontinuityrstatsstatastatisticssynthetic-controltreatment-effects

Score Breakdown

Stars 15% 338

338 stars → growing community

Freshness 25% 29d ago

Last commit 29d ago → recently updated

Issue Health 25% 100%

8/8 issues closed → responsive maintainer

Contributors 10% 6

6 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 72

Description Detailed

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

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Details

Stars: 338
Forks: 72
Open Issues: 0
Closed Issues: 8
Contributors: 6
Dependents: —
Language: Python
License: MIT
Last Commit: 29d ago

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From the README

简体中文（默认） | [English](https://github.com/brycewang-stanford/statspai/blob/main/README_EN.md)

<p align="center">
  
</p>

# StatsPAI：面向 Agent 设计的 Stata/R 平替

<p align="center">
  
  &nbsp;&nbsp;&nbsp;&nbsp;
  <a href="https://doi.org/10.21105/joss.10604"></a>
  <br>
  由 <strong>Stanford REAP</strong> 团队开发和维护
  <br>
  论文已发表于 <strong><em>Journal of Open Source Software</em>（JOSS）</strong>：Wang &amp; Rozelle (2026), 11(125), 10604 · <a href="https://doi.org/10.21105/joss.10604">doi:10.21105/joss.10604</a>
</p>

StatsPAI 面向那些原本需要在 Stata、R 和 Python 之间来回切换的实证研究者。它的目标很直接：把常见的计量经济学、因果推断、诊断、稳健性、表格导出和 Agent 可读元数据，放到一个 Python-native API 里。

你可以把它理解成新项目的 Stata/R 平替入口：

- Stata 风格：`regress`、`ivregress`、`reghdfe`、`csdid`、`rdrobust`、`synth`、`psmatch2`、`esttab` / `outreg2`。
- R 风格：`lm`、`fixest`、`did`、`rdrobust`、`Synth`、`DoubleML`、`MatchIt`、`modelsummary`、`broom`。
- 关键处遵循 Stata 约定：`regress`、`ivreg` 和各类似然估计器都接受 `vce="robust"` / `vce="cluster firm"`，稳健 / 聚类标准误采用 Stata 的小样本修正和 z / t 参考分布；任何拟合结果之后都可以做 `test

Read full README on GitHub →

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