Package: causalDT
Title: Causal Distillation Trees
Version: 1.0.1
Authors@R: c(
    person("Tiffany", "Tang", , "ttang4@nd.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8079-6867")),
    person("Melody", "Huang", , "melody.huang@yale.edu", role = "aut"),
    person("Ana", "Kenney", , "anamaria.kenney@uci.edu", role = "aut")
  )
Description: Causal Distillation Tree (CDT) is a novel machine learning
        method for estimating interpretable subgroups with
        heterogeneous treatment effects. CDT allows researchers to fit
        any machine learning model (or metalearner) to estimate
        heterogeneous treatment effects for each individual, and then
        "distills" these predicted heterogeneous treatment effects into
        interpretable subgroups by fitting an ordinary decision tree to
        predict the previously-estimated heterogeneous treatment
        effects. This package provides tools to estimate causal
        distillation trees (CDT), as detailed in Huang, Tang, and
        Kenney (2025) <doi:10.48550/arXiv.2502.07275>.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Depends: R (>= 4.1.0)
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
LinkingTo: Rcpp, RcppArmadillo
Imports: bcf, dplyr, ggparty, ggplot2, grf, lifecycle, partykit, purrr,
        R.utils, Rcpp, rlang, rpart, stringr, tibble, tidyselect
URL: https://tiffanymtang.github.io/causalDT/
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev
Repository: https://tiffanymtang.r-universe.dev
Date/Publication: 2026-08-28 19:14:29 UTC
RemoteUrl: https://github.com/tiffanymtang/causaldt
RemoteRef: HEAD
RemoteSha: e508f4de84cb98cf9478ad25cd9b575004b35819
RemoteSubdir: causalDT
NeedsCompilation: yes
Packaged: 2026-10-27 11:00:43 UTC; root
Author: Tiffany Tang [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8079-6867>),
  Melody Huang [aut],
  Ana Kenney [aut]
Maintainer: Tiffany Tang <ttang4@nd.edu>