{"repo":"brycewang-stanford/StatsPAI","free":true,"listed":false,"github":"https://github.com/brycewang-stanford/StatsPAI","clone":"git clone https://github.com/brycewang-stanford/StatsPAI.git","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.","language":"Python","stars":298,"topics":["agent-native","ai-agents","causal-discovery","causal-inference","data-science","difference-in-differences","double-machine-learning","econometrics","instrumental-variables","llm"],"license":"MIT","category":"ai-agents","readme_excerpt":"English 中文 StatsPAI: an Agent&Python-native Stata/R replacement for applied causal inference 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 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 , AER-Skills , Awesome-Journal-Skills , and 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 t","default_branch":null,"files":null,"tree":[],"storefront":"/r/brycewang-stanford","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/brycewang-stanford/StatsPAI/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}