An index of algorithms for learning causality with data

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

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

Causal discovery algorithms and tools for implementing new ones

💊 Comparing causality methods in a fair and just way.

Hyper-geometric computational causality library for Rust

Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.

causaleffect: R package for identifying causal effects.

Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024

Implementations of var-sortability, sortnregress, and chain-orientation as presented in the article "Beware of the Simulated DAG": https://arxiv.org/abs/2102.13647.

Predictive State Propensity Subclassification (PSPS): A causal deep learning algoritm in TensorFlow keras

Code for paper "A method for detecting causal relationships between industrial alarm variables using Transfer entropy and K2-Algorithm"

MR-link and genome integration. genome_integration is a repository for the analysis of genomic data. Specifically, the repository implements the causal inference method MR-link, as well as other Mendelian randomization methods.

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

dosearch: R Package for Identifying General Causal Queries

cfid: R package for identifying counterfactuals.

This R package is based on the work presented in A. Jérolon et al., "Causal mediation analysis in presence of multiple mediators uncausally related".The work allowing multiple mediation analyzes with a survival outcome was largely developed with Arce Domingo. This work is presented in Domingo-Relloso et al., "Causal mediation for uncausally relate

Code library for training causal inference deep learning models with automatic hyperparameter optimization written in Tensorflow 2.

A Powerful Python Library for Causal Inference

A broadcast middleware service delivering messages in a causal order