StatsPAI: Agent-Native Python Platform for Causal Inference and Econometrics

StatsPAI is presented as the first agent-native Python platform for causal inference and applied econometrics. It offers a unified API that covers a broad range of methods, including classical econometrics and AI/ML causal techniques. The platform provides structured result objects, machine-readable schemas, and R/Stata parity validation. Key features include over 1,000 registered functions, each with machine-readable discovery metadata, allowing AI agents to understand and utilize them effectively. Functions also expose explicit validation statuses, distinguishing certified numerical evidence from API-stable breadth. StatsPAI bundles classic teaching datasets, enabling offline execution of canonical causal inference exercises such as DiD, IV, RD, and Synthetic Control. The platform's result objects provide comprehensive inference scaffolding, including point estimates, standard errors, and diagnostic information, which would typically require combining outputs from multiple packages. It also supports reporting utilities for various formats like Word, Excel, and LaTeX.