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.

Experimental library integrating LLM capabilities to support causal analyses

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal …

[Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.

[Experimental] Global causal discovery algorithms

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Python package for (conditional) independence testing and statistical functions related to causality.

AutoML for causal inference.

Contains the code for https://py-why.github.io/