estimate_ite

Estimates the Individual Treatment Effect given a response vector,
a treatment vector, a covariate matrix, and a desired algorithm.

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 function

<dl><dt>y</dt>
<dd>An observed response vector.</dd>
<dt>z</dt>
<dd>An observed treatment vector.</dd>
<dt>X</dt>
<dd>A covariate matrix.</dd>
<dt>ite_method</dt>
<dd>A method for estimating the Individual Treatment Effect.
Some methods requires additional parameters. These parameters are mentioned
in the indented blocks for each method and their definitions are provided at
the end of this parameters list.<ul>
<li><code>"slearner"</code>: S-Learner.
- <code>learner_y</code></li>
<li><code>"tlearner"</code>: T-Learner.<ul>
<li><code>learner_y</code></li>
</ul></li>
<li><code>"tpoisson"</code>: T-Poisson.<ul>
<li><code>offset</code></li>
</ul></li>
<li><code>"xlearner"</code>: X-Learner.<ul>
<li><code>learner_y</code></li>
</ul></li>
<li><code>"aipw"</code>: Augmented Inverse Probability Weighting.<ul>
<li><code>learner_ps</code> and <code>learner_y</code></li>
</ul></li>
<li><code>"bart"</code>: Bayesian Additive Regression Trees.<ul>
<li><code>learner_ps</code></li>
</ul></li>
<li><code>"cf"</code>: Causal Forest.<ul>
<li><code>learner_ps</code></li>
</ul></li>
</ul></dd>
<dt>...</dt>
<dd>Additional parameters passed to different models.</dd></dl>

Arguments

Estimate the Individual Treatment Effect (ITE) — estimate_ite

<dl>

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


<dt>z</dt>
<dd>An observed treatment vector.</dd>


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


<dt>ite_method</dt>
<dd>A method for estimating the Individual Treatment Effect.
Some methods requires additional parameters. These parameters are mentioned
in the indented blocks for each method and their definitions are provided at
the end of this parameters list.<ul>
<li><code>"slearner"</code>: S-Learner.
- <code>learner_y</code></li>
<li><code>"tlearner"</code>: T-Learner.<ul>
<li><code>learner_y</code></li>
</ul></li>
<li><code>"tpoisson"</code>: T-Poisson.<ul>
<li><code>offset</code></li>
</ul></li>
<li><code>"xlearner"</code>: X-Learner.<ul>
<li><code>learner_y</code></li>
</ul></li>
<li><code>"aipw"</code>: Augmented Inverse Probability Weighting.<ul>
<li><code>learner_ps</code> and <code>learner_y</code></li>
</ul></li>
<li><code>"bart"</code>: Bayesian Additive Regression Trees.<ul>
<li><code>learner_ps</code></li>
</ul></li>
<li><code>"cf"</code>: Causal Forest.<ul>
<li><code>learner_ps</code></li>
</ul></li>
</ul></dd>


<dt>...</dt>
<dd>Additional parameters passed to different models.</dd>

</dl>

estimate_ite: Estimate the Individual Treatment Effect (ITE)

Description

Usage

Value

Arguments

Details

Additional parameters