Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

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

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

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"

[Experimental] Global causal discovery algorithms

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

Automated Bayesian model discovery for time series data

Scalable open-source software to run, develop, and benchmark causal discovery algorithms

Graph Optimiser for Learning and Evolution of Models

Amortized Inference for Causal Structure Learning, NeurIPS 2022

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks

[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution

Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks".

Sum-Product Network learning routines in python

Bayesian network structure learning

[SDM'23] ML4C: Seeing Causality Through Latent Vicinity

The source code repository for the FactorBase system

Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.

dagrad is a Python package that provides an extensible, modular platform for developing and experimenting with differentiable (gradient-based) structure learning methods.