↙️ ↘️ An R package for working with causal directed acyclic graphs (DAGs)

DoubleML - Double Machine Learning in R

WeightIt: an R package for propensity score weighting

Policy learning via doubly robust empirical welfare maximization over trees

Covariate Balance Tables and Plots - An R package for assessing covariate balance

📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮

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

Variable importance through targeted causal inference, with Alan Hubbard

A framework to infer causality on a pair of time series of real numbers based on Variable-lag Granger causality and transfer entropy.

Tutorial for a target trial emulation with a time-varying exposure, time-dependent confounding, time-to-event outcome, and Sequentially Doubly Robust estimation (Hoffman et al. 2022).

🎯🎓 Generalized Targeted Learning Framework

This repository provides R-code for the estimation of the conditional average treatment effect (CATE) using machine learning (ML) methods.

Methods for subgroup identification / personalized medicine / individualized treatment rules

R Package for "Matching on generalized propensity scores with continuous exposures". An innovative approach for estimating causal effects using observational data in settings with continuous exposures, and a new framework for GPS caliper matching that jointly matches on both the estimated GPS and exposure levels to fully adjust for confounding b…

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

The Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT)

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

R Package: Augmented Inverse Probability Weighted (AIPW) Estimation for Average Causal Effect