predict_teacher_models

predict_causal_forest

predict_bcf

These functions are <code><a href="/link/predict()?package=causalDT&version=1.0.0" data-mini-rdoc="causalDT::predict()">predict()</a></code> method wrappers for various heterogeneous
treatment effect learners that can be easily used as
teacher models in the causal distillation tree framework.<ul>
<li><code>predict_causal_forest()</code>: wrapper around <code><a href="/link/predict()?package=causalDT&version=1.0.0" data-mini-rdoc="causalDT::predict()">predict()</a></code> for
<code>causal_forest()</code> models.</li>
<li><code>predict_bcf()</code>: wrapper around <code><a href="/link/predict()?package=causalDT&version=1.0.0" data-mini-rdoc="causalDT::predict()">predict()</a></code> for
<code>bcf()</code> models.</li>
</ul>

internal

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

predict_teacher_models function

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

Arguments

These functions are <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> method wrappers for various heterogeneous
treatment effect learners that can be easily used as
teacher models in the causal distillation tree framework.<ul>
<li><code>predict_causal_forest()</code>: wrapper around <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> for
<code>causal_forest()</code> models.</li>
<li><code>predict_bcf()</code>: wrapper around <code><a href='https://rdrr.io/r/stats/predict.html'>predict()</a></code> for
<code>bcf()</code> models.</li>
</ul>

Predict wrappers for teacher models for causal distillation trees — predict_teacher_models

<dl>

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

</dl>

predict_teacher_models: Predict wrappers for teacher models for causal distillation trees

Description

Usage

Value

Arguments