estimate_cate

Estimates the Conditional Average Treatment Effect (CATE) by
linearly modeling the Individual Treatment Effect by a set of rules.

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

estimate_cate function

<dl><dt>rules_matrix</dt>
<dd>A rules matrix.</dd>
<dt>rules_explicit</dt>
<dd>A list of select rules in terms of covariate names.</dd>
<dt>ite</dt>
<dd>The estimated ITEs.</dd>
<dt>B</dt>
<dd>The number of bootstrap samples for uncertainty quantification in
estimation.</dd>
<dt>subsample</dt>
<dd>The bootstrap ratio subsample for uncertainty quantification
in estimation.</dd></dl>

Arguments

Estimate the Conditional Average Treatment Effect — estimate_cate

<dl>

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


<dt>rules_explicit</dt>
<dd>A list of select rules in terms of covariate names.</dd>


<dt>ite</dt>
<dd>The estimated ITEs.</dd>


<dt>B</dt>
<dd>The number of bootstrap samples for uncertainty quantification in
estimation.</dd>


<dt>subsample</dt>
<dd>The bootstrap ratio subsample for uncertainty quantification
in estimation.</dd>

</dl>

estimate_cate: Estimate the Conditional Average Treatment Effect

Description

Usage

Value

Arguments