Bayesian Networks Python Packages

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

compiled-knowledge

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

pybnesian

PyBNesian is a Python package that implements Bayesian networks.

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"

causalnex

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

neural-tangents

Fast and Easy Infinite Neural Networks in Python

bamt-light

Repository of a 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

iscan-dag

A Python 3 package for identifying distribution shifts (a.k.a feature-shifts) between datasets. Official implementation of the paper: "iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models".

pgmpy-rcr

Python Toolkit for Causal and Probabilistic Reasoning

scikit-bayes

A Python package for AnDE classifiers.

cstrees

CSlearn: a package for context-specific causal models

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).