QuentinAndre/pyprocessmacro

Causal Inference in Python

Single-level and multi-level mediation analyses for any kind of data, with bootstrap-based significance testing. Neuroimaging-oriented functions allow for mediation effect parametric mapping (mapping of mediation effects across the brain) and multivariate mediation.

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.

Structural causal models with Bayesian estimation and interventional simulation via a concise DSL.

DoubleML - Double Machine Learning in Python

Generalized Linear Models in Sklearn Style

A Python package for causal inference in quasi-experimental settings

A concise syntax to describe and execute routine data analysis tasks

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.