generate_rules_matrix

Generates the rules matrix from the feature covariate matrix and a vector of
rules. The number of rows in rules_matrix is equal to the number of samples
in <code>X</code>, and the number of columns is equal to the number of rules in
<code>rules_list</code>. Each element of rules_matrix corresponds to a specific data
sample and rule. If the data sample satisfies a rule, the corresponding
element in rules_matrix is set to 1. Otherwise, the element is set to 0.

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

generate_rules_matrix function

<dl><dt>X</dt>
<dd>Features matrix.</dd>
<dt>rules_list</dt>
<dd>A vector of rules.</dd></dl>

Arguments

Generate rules matrix — generate_rules_matrix

<dl>

<dt>X</dt>
<dd>Features matrix.</dd>


<dt>rules_list</dt>
<dd>A vector of rules.</dd>

</dl>

generate_rules_matrix: Generate rules matrix

Description

Usage

Value

Arguments