constructor_shared

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

constructor_shared function

<dl><dt>info</dt>
<dd>(character) Optional information to describe the instantiated
learner object.</dd>
<dt>formula</dt>
<dd>(formula) Formula specifying response and design matrix.</dd>
<dt>learner.args</dt>
<dd>(list) Additional arguments to
learner$new().</dd></dl>

Arguments

Construct a learner — constructor_shared

<dl>

<dt>info</dt>
<dd>(character) Optional information to describe the instantiated
learner object.</dd>


<dt>formula</dt>
<dd>(formula) Formula specifying response and design matrix.</dd>


<dt>learner.args</dt>
<dd>(list) Additional arguments to
learner$new().</dd>

</dl>

constructor_shared: Construct a learner

Description

Value

Arguments