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.

thegolem

Graph Optimiser for Learning and Evolution of Models

bamt

Repository of a data modeling and analysis tool based on 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"

avici

Amortized Inference for Causal Structure Learning, NeurIPS 2022

bnclassify

bnlearn

dibs-lib

DiBS: Differentiable Bayesian Structure Learning

py-bnlearn

bnlearn is a Python package for Causal Discovery by learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

bamt-light

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

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.

iscm

Standardizing Structural Causal Models, ICLR 2025