StatsPAI — MCP Server by brycewang-stanford

by brycewang-stanford · MCP Server · ★ 292

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About StatsPAI

English | 中文 StatsPAI: Agent-Native Causal Inference & Econometrics Toolkit for Python [](https://doi

agent-nativeai-agentscausal-discoverycausal-inferencedata-sciencedifference-in-differencesdouble-machine-learningeconometricsinstrumental-variablesllm

Quick Facts

Stars292
Forks62
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score63.1666562408099/100
Open Issues3
Last Updated2026-08-07
Created2025-07-26
Platformsmcp, python
Est. Tokens~25k

Compatible Skills

These tools work well together with StatsPAI for enhanced workflows:

  • DeepEye — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • stata-skill — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (57%)
  • stata-mcp — semantic(0.35)+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • statsclaw — semantic(0.30)+complementary+rare_topics+similar_pop (56%)

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Frequently Asked Questions

What is StatsPAI?

StatsPAI is 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,. It is categorized as a MCP Server with 292 GitHub stars.

What programming language is StatsPAI written in?

StatsPAI is primarily written in Python. It covers topics such as agent-native, ai-agents, causal-discovery.

How do I install or use StatsPAI?

You can find installation instructions and usage details in the StatsPAI GitHub repository at github.com/brycewang-stanford/StatsPAI. The project has 292 stars and 62 forks, indicating an active community.

What license does StatsPAI use?

StatsPAI is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to StatsPAI?

The top alternatives to StatsPAI on Agent Skills Hub include wisp-science, BambooAI, web-agent-protocol. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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