rlearner_teacher

This is a wrapper function to convert any of the <code>rlearner</code>
model functions into a format that can be used as teacher model in the
causal distillation tree framework.

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

rlearner_teacher function

<dl><dt>rlearner_fun</dt>
<dd>One of <code>rlearner::rboost</code>,
<code>rlearner::rlasso</code>, or <code>rlearner::rkern</code> to be transformed to
teacher model format for CDT.</dd>
<dt>...</dt>
<dd>Additional arguments to pass to the base model functions.</dd></dl>

Arguments

Rlearner teacher model wrapper for causal distillation trees — rlearner_teacher

<dl>

<dt>rlearner_fun</dt>
<dd>One of <code>rlearner::rboost</code>,
<code>rlearner::rlasso</code>, or <code>rlearner::rkern</code> to be transformed to
teacher model format for CDT.</dd>


<dt>...</dt>
<dd>Additional arguments to pass to the base model functions.</dd>

</dl>

rlearner_teacher: Rlearner teacher model wrapper for causal distillation trees

Description

Usage

Value

Arguments