Next generation of automated data exploratory analysis and visualization platform.

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

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Must-read papers and resources related to causal inference and machine (deep) learning

YLearn, a pun of "learn why", is a python package for causal inference

Python package for causal discovery based on LiNGAM.

A resource list for causality in statistics, data science and physics

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Causal discovery algorithms and tools for implementing new ones

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

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"

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

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

Amortized Inference for Causal Structure Learning, NeurIPS 2022

Active Bayesian Causal Inference (Neurips'22)

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks

가짜연구소 <인과추론과 실무> 프로젝트

R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test