estimate_ite_aipw

Estimates the Individual Treatment Effect using Augmented Inverse Probability
Weighting given a response vector, a treatment vector, a features matrix,
an estimation model for the propensity score and estimation model for the
outcome.

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_ite_aipw function

<dl><dt>y</dt>
<dd>An observed response vector.</dd>
<dt>z</dt>
<dd>A treatment vector.</dd>
<dt>X</dt>
<dd>A features matrix.</dd>
<dt>learner_ps</dt>
<dd>A estimation model for the propensity score.</dd>
<dt>learner_y</dt>
<dd>A estimation model for the outcome.</dd></dl>

Arguments

Estimate the Individual Treatment Effect (ITE) using Augmented Inverse
Probability Weighting (AIPW) — estimate_ite_aipw

<dl>

<dt>y</dt>
<dd>An observed response vector.</dd>


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


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


<dt>learner_ps</dt>
<dd>A estimation model for the propensity score.</dd>


<dt>learner_y</dt>
<dd>A estimation model for the outcome.</dd>

</dl>

estimate_ite_aipw: Estimate the Individual Treatment Effect (ITE) using Augmented Inverse Probability Weighting (AIPW)

Description

Usage

Value

Arguments