weights.superlearner

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>),
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

weights.superlearner function

<dl><dt>object</dt>
<dd>(superlearner) Fitted model.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Extract ensemble weights — weights.superlearner

<dl>

<dt>object</dt>
<dd>(superlearner) Fitted model.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

weights.superlearner: Extract ensemble weights

Description

Usage

Arguments