causalmlr - Causal Machine Learning in R

Estimation and evaluation of average and conditional average treatment effects (ATE/CATE) from observational data using machine learning. Provides classical estimators (naive difference in means, inverse propensity weighting, doubly robust / AIPW, double machine learning) and meta-learners (S-, T- and X-Learner), all built on top of the 'mlr3' ecosystem so that any regression or classification learner can be plugged in as a nuisance model. Also includes causal model evaluation utilities (ATE error, PEHE, R-Loss) with optional cross-fitting, and a collection of benchmark datasets commonly used for teaching machine learning for causal inference.