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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "causalDT" in publications use:'
type: software
license: MIT
title: 'causalDT: Causal Distillation Trees'
version: 1.0.0
doi: 10.32614/CRAN.package.causalDT
abstract: 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) <https://doi.org/10.48550/arXiv.2502.07275>.
authors:
- family-names: Tang
  given-names: Tiffany
  email: ttang4@nd.edu
  orcid: https://orcid.org/0000-0002-8079-6867
- family-names: Huang
  given-names: Melody
  email: melody.huang@yale.edu
- family-names: Kenney
  given-names: Ana
  email: anamaria.kenney@uci.edu
repository: https://tiffanymtang.r-universe.dev
commit: 2a58e5a5fef4eb05cae83eb4a9dd4ce6d8c1fbdd
url: https://tiffanymtang.github.io/causalDT/
date-released: '2026-05-30'
contact:
- family-names: Tang
  given-names: Tiffany
  email: ttang4@nd.edu
  orcid: https://orcid.org/0000-0002-8079-6867