evaluate

Computes different metrics for discovery evaluation.

internal

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

evaluate function

<dl><dt>ground_truth</dt>
<dd>List of true (CDR/EM) values.</dd>
<dt>prediction</dt>
<dd>List of predicted (CDR/EM) values.</dd></dl>

Arguments

Discovery (performance) evaluation — evaluate

<dl>

<dt>ground_truth</dt>
<dd>List of true (CDR/EM) values.</dd>


<dt>prediction</dt>
<dd>List of predicted (CDR/EM) values.</dd>

</dl>

evaluate: Discovery (performance) evaluation

Description

Usage

Value

Arguments