filter_extreme_rules

Discards rules with too few or too many observations.

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_extreme_rules function

<dl><dt>rules_matrix</dt>
<dd>A rules matrix.</dd>
<dt>rules_list</dt>
<dd>A list of rules (names).</dd>
<dt>t_ext</dt>
<dd>A threshold in [0, 0.5) range to truncate too generic or too
specific rules.</dd></dl>

Arguments

Filter extreme decision rules — filter_extreme_rules

<dl>

<dt>rules_matrix</dt>
<dd>A rules matrix.</dd>


<dt>rules_list</dt>
<dd>A list of rules (names).</dd>


<dt>t_ext</dt>
<dd>A threshold in [0, 0.5) range to truncate too generic or too
specific rules.</dd>

</dl>

filter_extreme_rules: Filter extreme decision rules

Description

Usage

Value

Arguments