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causalDT  

Causal Distillation Trees
View on CRAN: Click here


Download and install causalDT package within the R console
Install from CRAN:
install.packages("causalDT")

Install from Github:
library("remotes")
install_github("cran/causalDT")

Install by package version:
library("remotes")
install_version("causalDT", "1.0.0")



Attach the package and use:
library("causalDT")
Maintained by
Tiffany Tang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-09-03
Latest Update: 2025-09-03
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>.
How to cite:
Tiffany Tang (2025). causalDT: Causal Distillation Trees. R package version 1.0.0, https://cran.r-project.org/web/packages/causalDT. Accessed 12 Sep. 2026.
Previous versions and publish date:
(2026-08-28 22:00), 1.0.0 (2025-09-03 10:00)
Other packages that cited causalDT R package
View causalDT citation profile
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Complete documentation for causalDT
Functions, R codes and Examples using the causalDT R package
Full causalDT package functions and examples
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