get_rpart_tree_info

Return the split information for each node in an <code>rpart</code> model as a data frame.

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_tree_info function

<dl><dt>rpart_fit</dt>
<dd>An <code>rpart</code> object.</dd>
<dt>X</dt>
<dd>Optional data frame containing the features used in the <code>rpart</code>
model. Only used if the model contains categorical variables.</dd>
<dt>digits</dt>
<dd>Number of digits to round the split values to.</dd></dl>

Arguments

Get split information from an rpart model. — get_rpart_tree_info

<dl>

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


<dt>X</dt>
<dd>Optional data frame containing the features used in the <code>rpart</code>
model. Only used if the model contains categorical variables.</dd>


<dt>digits</dt>
<dd>Number of digits to round the split values to.</dd>

</dl>

get_rpart_tree_info: Get split information from an rpart model.

Description

Usage

Value

Arguments