estimate_ps

Estimates the Propensity Score given a treatment vector and
features data frame.

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_ps function

<dl><dt>z</dt>
<dd>A treatment vector.</dd>
<dt>X</dt>
<dd>A features data frame.</dd>
<dt>ps_method</dt>
<dd>An estimation model for the propensity score
(default: <code>SL.xgboost</code>).</dd></dl>

Arguments

Estimate the propensity score — estimate_ps

<dl>

<dt>z</dt>
<dd>A treatment vector.</dd>


<dt>X</dt>
<dd>A features data frame.</dd>


<dt>ps_method</dt>
<dd>An estimation model for the propensity score
(default: <code>SL.xgboost</code>).</dd>

</dl>

estimate_ps: Estimate the propensity score

Description

Usage

Value

Arguments