A Python library for moderation, mediation and conditional process analysis.

Causing: CAUsal INterpretation using Graphs

R implementation of effect measure modification-extended regression-based closed-formula causal mediation analysis

Fast regression and mediation analysis of vertex or voxel MRI data with TFCE

Flexible mediation analysis using natural effect models in R

The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis

📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects

Plain-language guide to causal inference for microbiome & multi-omics: DAGs/backdoor, confounders vs mediators/colliders, causal mediation (ACME/ADE/Total), and Double Machine Learning (DML) with toy examples + code.

R/medltmle: Estimation and Inference for Natural Mediation Effect in Longitudinal Data

jamovi Advanced Mediation Models

MR project investigating the mediating role of mammographic density in the childhood body size and breast cancer relationship

small R utility for mediation analysis

Causal Mediation analysis

📦 🎲 R/medshift: Causal Mediation Analysis for Stochastic Interventions

Implementations of "Testing individual mediation effects using the logic of Boolean matrix" (JASA 2022) and "Sequential pathway inference for multimodal neuroimaging analysis" (STAT 2022) in Python

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

R package to conduct mediation analysis using joint significance.

The Simplest and Most Correct Way to Do Causal Mediation Analysis

📦R package medRCT

🎯 🔀 Targeted Learning for Causal Mediation Analysis