generate_rules

Generates a list of rules characterizing the heterogeneity in the Conditional
Average Treatment Effect (CATE) by tree-based methods (i.e., random forest).

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 function

<dl><dt>X</dt>
<dd>A covariate matrix.</dd>
<dt>ite</dt>
<dd>A vector of estimated ITE.</dd>
<dt>ntrees</dt>
<dd>The number of decision trees for the random forest algorithm.</dd>
<dt>node_size</dt>
<dd>Minimum size of the trees' terminal nodes.</dd>
<dt>max_rules</dt>
<dd>Maximum number of candidate decision rules.</dd>
<dt>max_depth</dt>
<dd>Maximum rules length.</dd></dl>

Arguments

Generate rules — generate_rules

<dl>

<dt>X</dt>
<dd>A covariate matrix.</dd>


<dt>ite</dt>
<dd>A vector of estimated ITE.</dd>


<dt>ntrees</dt>
<dd>The number of decision trees for the random forest algorithm.</dd>


<dt>node_size</dt>
<dd>Minimum size of the trees' terminal nodes.</dd>


<dt>max_rules</dt>
<dd>Maximum number of candidate decision rules.</dd>


<dt>max_depth</dt>
<dd>Maximum rules length.</dd>

</dl>

generate_rules: Generate rules

Description

Usage

Value

Arguments