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StatsPAI
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StatsPAI

Active·★ 343·MIT·Updated 2026-10-21
★ Hidden Gem

StatsPAI is a Python-native library designed for empirical researchers, aiming to replace common Stata and R econometric and causal inference workflows. It offers a unified API for data loading, model estimation, diagnostics, and result export, facilitating a seamless transition for Python-first work.

StatsPAI sits in the Workflow Automation category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Offers unified Stata/R-style econometric and causal inference routines in Python.. A representative use case is Migrating existing Stata and R econometric and causal inference workflows to a Python environment.. The listed license is MIT. GitHub stars are currently 343. Relevant tags include Causal Inference, Econometrics.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool.

#Causal Inference#Econometrics#Python#Stata-replacement#R-replacement#Statistical Modeling#Data Analysis#Agent-enabled
$ Install
$ pip install statspai
↗ Visit site★ GitHub
01

Features

01Offers unified Stata/R-style econometric and causal inference routines in Python.
02Provides native Python output methods for summaries, tidy dataframes, and plots.
03Exports publication-quality regression tables to Excel or Word documents.
04Includes an interactive plot editor with reproducible code generation for Jupyter environments.
05Features comprehensive validation status and cross-language parity checks against Stata/R.
02

Why choose it

+Offers unified Stata/R-style econometric and causal inference routines in Python.
+Provides native Python output methods for summaries, tidy dataframes, and plots.
+Migrating existing Stata and R econometric and causal inference workflows to a Python environment.
+Comes with a clearly listed MIT license.
03

Trade-offs

!Compatibility coverage appears narrow, so your target environment should be validated before adoption.
04

Compatibility

Python
Runtime
Verified via docs
Jupyter
Environment
Verified via docs
05

Quick start

1
$ pip install statspai
06

Use cases

↳Migrating existing Stata and R econometric and causal inference workflows to a Python environment.
↳Conducting applied causal inference research using a Python-native statistical framework.
↳Generating high-quality regression tables and interactive plots for academic publications.
↳Automating empirical research tasks by integrating econometric analysis with AI agents.
↳Cross-validating statistical model results with established Stata and R packages.
07

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07Alternatives
brycewang-stanford /
StatsPAI
↗

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.

343MIT
Last commit2d ago
StatusActive
CategoryWorkflow Automation
Links
Documentation↗Discussion↗Issues↗Releases↗
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