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

Andreas Nordland

SL function

<dl><dt>formula</dt>
<dd>Model design</dd>
<dt>...</dt>
<dd>Additional arguments for SuperLearner::SuperLearner</dd>
<dt>SL.library</dt>
<dd>character vector of prediction algorithms</dd>
<dt>binomial</dt>
<dd>boolean specifying binomial or gaussian family (default FALSE)</dd>
<dt>data</dt>
<dd>Optional data.frame</dd></dl>

Arguments

Author

SuperLearner wrapper for ml_model — SL

<dl>

<dt>formula</dt>
<dd>Model design</dd>


<dt>...</dt>
<dd>Additional arguments for SuperLearner::SuperLearner</dd>


<dt>SL.library</dt>
<dd>character vector of prediction algorithms</dd>


<dt>binomial</dt>
<dd>boolean specifying binomial or gaussian family (default FALSE)</dd>


<dt>data</dt>
<dd>Optional data.frame</dd>

</dl>

SL: SuperLearner wrapper for ml_model

Description

Usage

Value

Arguments

Author