metalearner_discrete

Implements the discrete super learner: the candidate learner
with the lowest risk (computed via the <code>model.score</code> argument of
superlearner) is given weight one and all other learners weight zero.

Various methods for targeted and semiparametric inference including
augmented inverse probability weighted (AIPW) estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
one-step imputation (Nordland et al (2026)) <doi:10.48550/arXiv.2606.07174>),
variable importance and conditional average treatment effects (CATE)
(van der Laan (2006) <doi:10.2202/1557-4679.1008>),
estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Klaus Holst

targeted

Targeted Inference

Klaus K. Holst

Benedikt Sommer

Andreas Nordland

Christian B. Pipper

metalearner_discrete function

<dl><dt>y</dt>
<dd>(numeric) Response vector.</dd>
<dt>pred</dt>
<dd>(matrix) Matrix of cross-validated predictions with one column
per candidate learner.</dd>
<dt>model.score</dt>
<dd>(function) Method for scoring the predictions of each base
learner.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Discrete meta learner — metalearner_discrete

<dl>

<dt>y</dt>
<dd>(numeric) Response vector.</dd>


<dt>pred</dt>
<dd>(matrix) Matrix of cross-validated predictions with one column
per candidate learner.</dd>


<dt>model.score</dt>
<dd>(function) Method for scoring the predictions of each base
learner.</dd>


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

metalearner_discrete: Discrete meta learner

Description

Usage

Value

Arguments

See Also