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 Causal AI

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

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

Fast and Easy Infinite Neural Networks in Python

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 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"

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

Bayesian Network Modeling and Analysis

A Java Toolbox for Scalable Probabilistic Machine Learning

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

Python tools for analyzing both classical and quantum Bayesian Networks

Scalable open-source software to run, develop, and benchmark causal discovery algorithms

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

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

PyBNesian is a Python package that implements Bayesian networks.

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

Software for learning sparse Bayesian networks

Online tool for Bayesian Networks

R Wrapper for Tetrad Library

Risk Network Modeling and Analysis

Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Bayesian-Active-Learning/))

Multi-purpose data analysis framework based on Bayesian networks and Causal models

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection (Physionet Challenge 2022)

Learning Discrete Bayesian Network Classifiers from Data

COBAYN: Compiler Autotuning Framework Using Bayesian Networks

R package for inference in Bayesian networks.

dbnlearn: An R package for Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting