Package: causalDT
Title: Causal Distillation Trees
Version: 1.0.0
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
RoxygenNote: 7.3.2
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/
NeedsCompilation: yes
Packaged: 2025-08-28 20:55:06 UTC; ttang4
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>
Repository: CRAN
Date/Publication: 2025-09-03 08:00:13 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 13:37:22 UTC; unix
Archs: causalDT.so.dSYM
