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pgmpy

Python Toolkit for Causal and Probabilistic Reasoning

dowhy

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.

bamt

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

pybnesian

PyBNesian is a Python package that implements Bayesian networks.

compiled-knowledge

CompiledKnowledge is a Python package for compiling and querying discrete probabilistic graphical models.

neural-tangents

Fast and Easy Infinite Neural Networks in Python

dagma

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"

bamt-light

data modeling and analysis tool based on Bayesian networks

pycid

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

dibs-lib

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

pgmpy-rcr

Python Toolkit for Causal and Probabilistic Reasoning

iscan-dag

Implementation of the iSCAN algorithm for detecting distribution shifts

cstrees

CSlearn: a package for context-specific causal models

scikit-bayes

A Python package for AnDE classifiers.

junctiontree

The junction tree algorithm for (discrete) factor graphs

daft-builder

Wrapper library on daft that provides a builder interface for rendering probabilistic graphical models (PGMs).