tmp_method.CC_nloglik

<p>Temporary fix for convex combination method negative log-likelihood loss
Relative to existing implementation, we reduce the tolerance at which
we declare predictions from a given algorithm the same as another.
Note that because of the way <code>SuperLearner</code> is structure, one needs to
install the optimization software separately.</p>

Targeted minimum loss-based estimators of counterfactual means and
causal effects that are doubly-robust with respect both to consistency and
asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

David Benkeser

drtmle

Doubly-Robust Nonparametric Estimation and Inference

Nima Hejazi

tmp_method.CC_nloglik function

Temporary fix for convex combination method negative log-likelihood loss
Relative to existing implementation, we reduce the tolerance at which
we declare predictions from a given algorithm the same as another.
Note that because of the way SuperLearner is structure, one needs to
install the optimization software separately. — tmp_method.CC_nloglik

Temporary fix for convex combination method negative log-likelihood loss
Relative to existing implementation, we reduce the tolerance at which
we declare predictions from a given algorithm the same as another.
Note that because of the way <code>SuperLearner</code> is structure, one needs to
install the optimization software separately.

Description

Usage

Arguments