predict.cre

Predicts individual treatment effect via causal rule ensemble algorithm.

Provides a new method for interpretable heterogeneous
treatment effects characterization in terms of decision rules
via an extensive exploration of heterogeneity patterns by an
ensemble-of-trees approach, enforcing high stability in the
discovery. It relies on a two-stage pseudo-outcome regression, and
it is supported by theoretical convergence guarantees. Bargagli-Stoffi,
F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble:
Interpretable Discovery and Inference of Heterogeneous Treatment Effects.
arXiv preprint <doi:10.48550/arXiv.2009.09036>.

Falco Joannes Bargagli Stoffi

Interpretable Discovery and Inference of Heterogeneous Treatment
Effects

Naeem Khoshnevis

Daniela Maria Garcia

Riccardo Cadei

Kwonsang Lee

predict.cre function

<dl><dt>object</dt>
<dd>A <code>cre</code> object from running the CRE function.</dd>
<dt>X</dt>
<dd>A covariate matrix (or data.frame)</dd>
<dt>...</dt>
<dd>Additional arguments passed to customize the prediction.</dd></dl>

Arguments

Predict individual treatment effect via causal rule ensemble — predict.cre

<dl>

<dt>object</dt>
<dd>A <code>cre</code> object from running the CRE function.</dd>


<dt>X</dt>
<dd>A covariate matrix (or data.frame)</dd>


<dt>...</dt>
<dd>Additional arguments passed to customize the prediction.</dd>

</dl>

predict.cre: Predict individual treatment effect via causal rule ensemble

Description

Usage

Value

Arguments