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.

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

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

A Java Toolbox for Scalable Probabilistic Machine Learning

Bayesian Network Modeling and Analysis

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

Library for graphical models of decision making, based on pgmpy and networkx

Python tools for analyzing both classical and quantum Bayesian Networks

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

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

Software for learning sparse Bayesian networks

An implementation of Bayesian Networks Model for pure C++14 (11) later, including probability inference and structure learning method.

Online tool for Bayesian Networks

R Wrapper for Tetrad Library