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 |
| DOI: |
10.32614/CRAN.package.causalDT |
| Author: |
Tiffany Tang 
[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:
Downloads:
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