Structure Learning Python Packages

bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

bamt

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

thegolem

Graph Optimiser for Learning and Evolution of Models

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"

avici

Amortized Inference for Causal Structure Learning, NeurIPS 2022

andrey-core

A very fast causal discovery package

bamt-light

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

dibs-lib

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

qdscreen

Quasi-determinism screening for fast Bayesian Network Structure Learning (from T.Rahier's PhD thesis, 2018)

spacial-boxcounting

A spacial boxcount algorithm is proposed, which encodes incoming data into scaled down version of itself at diffrent scales discribing spacial resolved complexity and heterogenity.

bnclassify

bnlearn

iscm

Standardizing Structural Causal Models, ICLR 2025

py-bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.