Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

ABSTRACT: Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we bench

SUBMITTER: Li H 

PROVIDER: S-EPMC10362909 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature