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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.

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

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.

Sparse nonlinear least squares in JAX

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

Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021