pgmpy

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.

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Fast and Easy Infinite Neural Networks in Python

A Python library that helps data scientists to infer causation rather than observing correlation.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Causal discovery algorithms and tools for implementing new ones

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

A web app to create and browse text visualizations for automated customer listening.

Repository of a data modeling and analysis tool based on Bayesian networks

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)