An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

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

An R script to perform two sample Mendelian randomization screening (with TwoSampleMR) for a custom summary statistic against a set of summary statistics from the IEU GWAS database.

Causal Deconvolution of Networks by Algorithmic Generative Models

Finding the causality in biological pathways

The simMixedDAG package enables simulation of "real life" datasets from DAGs

Estimates the inference of a Fuzzy Cognitive Map (FCM). Provides a selection of 6 different inference rules and 4 threshold functions in order to obtain the inference of the FCM.

Cyclic Causal Inference

Causal Inference over Mixtures

dosearch: R Package for Identifying General Causal Queries

Tools for sensitivity analysis for weighted estimators

The orientDAG package is used to orient DAG edges. It also includes utility functions to convert DAGs between different representations as well as measure DAG dissimilarity measures.

[ICML 2025] R implementation of MIIC_search&score: a search-and-score algorithm for learning ancestral graphs with latent confounders, using multivariate information over ac-connected subset.

The GFSM algorithm

🛸 The Directed Prediction Index (DPI): Quantifying Relative Endogeneity for Causal Inference from Observational Data.

CausalVerse: An R toolkit expediting causal research & analysis. Streamlines complex methodologies, empowering users to unveil causal relationships with precision. Your go-to for insightful causality exploration.

Shiny app illustrating the movie star example based on S. Cunningham "Causal Inference: The Mixtape" (Section 3.1.6)

Tool to extract causal relationships from biological and medical databases that are in tabular format

An R package for learning context-specific causal models, called CStrees, based on observational, or a mix of observational and interventional, data.

Provides some functions for calculating causal effects.