extract_rules

Extracts causal rules from the random forest or the gradient
boosting algorithms.

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

extract_rules function

<dl><dt>treelist</dt>
<dd>A list of decision trees.</dd>
<dt>X</dt>
<dd>Features matrix.</dd>
<dt>max_depth</dt>
<dd>A number of top levels from each tree considered.</dd>
<dt>digits</dt>
<dd>A Number of digits for rounding decision rules to extract
conditions.</dd></dl>

Arguments

Extract (causal) decision rules — extract_rules

<dl>

<dt>treelist</dt>
<dd>A list of decision trees.</dd>


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


<dt>max_depth</dt>
<dd>A number of top levels from each tree considered.</dd>


<dt>digits</dt>
<dd>A Number of digits for rounding decision rules to extract
conditions.</dd>

</dl>

extract_rules: Extract (causal) decision rules

Description

Usage

Value

Arguments