An index of algorithms for learning causality with data

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

Hyper-geometric computational causality for Rust

💊 Comparing causality methods in a fair and just way.

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

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

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

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

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.

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

dosearch: R Package for Identifying General Causal Queries

Tutorials for the synthetic control method for causal inference using PyMC

Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.

Análise de Intervenção de Ansiedade com Descoberta Causal

Python package for CITS algorithm: Causal inference from time series data

Basic experimental set-up for the comparison of causal structure learning algorithms as shown in "Beware of the Simulated DAG".

Identifiability of AMP chain graph