filter_irrelevant_rules

Filters the irrelevant decision rules. The irrelevant rules are interpreted
as an error increase after removing a variable-value pair from the decision
rules (see &#8220;Interpreting tree ensembles with the inTrees package&#8221; by
Houtao Deng, 2019).

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

filter_irrelevant_rules function

<dl><dt>rules</dt>
<dd>A list of rules.</dd>
<dt>X</dt>
<dd>A features matrix.</dd>
<dt>ite</dt>
<dd>An estimated ITE.</dd>
<dt>t_decay</dt>
<dd>The decay threshold for rules pruning.</dd></dl>

Arguments

Filter irrelevant decision rules using leave-one-out pruning — filter_irrelevant_rules

<dl>

<dt>rules</dt>
<dd>A list of rules.</dd>


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


<dt>ite</dt>
<dd>An estimated ITE.</dd>


<dt>t_decay</dt>
<dd>The decay threshold for rules pruning.</dd>

</dl>

filter_irrelevant_rules: Filter irrelevant decision rules using leave-one-out pruning

Description

Usage

Value

Arguments