G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes.

ABSTRACT: In clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of

SUBMITTER: Le Borgne F 

PROVIDER: S-EPMC7809122 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature