get_rpart_paths

Return the decision paths for each leaf node in an <code>rpart</code> model as character
strings.

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>.

Tiffany Tang

causalDT

Causal Distillation Trees

Melody Huang

Ana Kenney

get_rpart_paths function

<dl><dt>rpart_fit</dt>
<dd>An <code>rpart</code> object.</dd></dl>

Arguments

Get decision paths from an rpart model. — get_rpart_paths

<dl>

<dt>rpart_fit</dt>
<dd>An <code>rpart</code> object.</dd>

</dl>

get_rpart_paths: Get decision paths from an rpart model.

Description

Usage

Value

Arguments