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cff-version: 1.2.0
message: 'To cite package "causalmlr" in publications use:'
type: software
license: LGPL-2.0-or-later
title: 'causalmlr: Causal Machine Learning in R'
version: 0.1.0
abstract: 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.
authors:
- family-names: Machlanski
  given-names: Damian
  email: d.machlanski@ed.ac.uk
repository: https://dmachlanski.r-universe.dev
repository-code: https://github.com/dmachlanski/causalmlr
commit: 110b181309a65d0e818c374fa8747f0043a149a7
url: https://github.com/dmachlanski/causalmlr
date-released: '2026-07-15'
contact:
- family-names: Machlanski
  given-names: Damian
  email: d.machlanski@ed.ac.uk