causalDT: Causal Distillation Trees

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>.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: bcf, dplyr, ggparty, ggplot2, grf, lifecycle, partykit, purrr, R.utils, Rcpp, rlang, rpart, stringr, tibble, tidyselect
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0)
Published: 2025-09-03
Author: Tiffany Tang ORCID iD [aut, cre], Melody Huang [aut], Ana Kenney [aut]
Maintainer: Tiffany Tang <ttang4 at nd.edu>
License: MIT + file LICENSE
URL: https://tiffanymtang.github.io/causalDT/
NeedsCompilation: yes
Materials: README
CRAN checks: causalDT results

Documentation:

Reference manual: causalDT.html , causalDT.pdf

Downloads:

Package source: causalDT_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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