Package: causalmlr
Type: Package
Title: Causal Machine Learning in R
Version: 0.1.0
Authors@R: 
    person("Damian", "Machlanski", email = "d.machlanski@ed.ac.uk",
           role = c("aut", "cre"))
Description: 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.
License: LGPL (>= 2.1)
URL: https://github.com/dmachlanski/causalmlr
BugReports: https://github.com/dmachlanski/causalmlr/issues
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
Depends: R (>= 4.1.0)
Imports: mlr3 (>= 0.14.0), stats
Suggests: knitr, lgr, mlr3learners, mlr3tuning, mlr3tuningspaces,
        ranger, rmarkdown, rpart, testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
Roxygen: list(markdown = TRUE)
Config/roxygen2/version: 8.0.0
Repository: https://dmachlanski.r-universe.dev
Date/Publication: 2026-07-15 12:10:54 UTC
RemoteUrl: https://github.com/dmachlanski/causalmlr
RemoteRef: main
RemoteSha: 110b181309a65d0e818c374fa8747f0043a149a7
NeedsCompilation: no
Packaged: 2026-07-15 17:00:46 UTC; root
Author: Damian Machlanski [aut, cre]
Maintainer: Damian Machlanski <d.machlanski@ed.ac.uk>