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

A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

DGMs for NLP. A roadmap.

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

Scikit-learn compatible estimation of general graphical models

Robopy is a python port for Robotics Toolbox in Matlab created by Peter Corke

Graphical language server platform for building web-based diagram editors

Scalable inference for a generative model of astronomical images

Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum

Input Output Hidden Markov Model (IOHMM) in Python

"Low ceremony" graphical modeling and code generation tool based on QP frameworks and Hierarchical State Machines (UML Statecharts)

Factored inference for discrete-continuous smoothing and mapping.

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

Kalman Variational Auto-Encoder

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"

Deep Markov Models

A toolbox for differentially private data generation

A Java Toolbox for Scalable Probabilistic Machine Learning