An improved multiply robust estimator for the average treatment effect.

ABSTRACT:

Background

In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However, the approaches are based on parametric models, leading to biased estimates when all models are incorrectly specified. Nonparametric methods, such as machine learning or nonparametric double robust approaches, are robust to model misspecification, but the efficiency of nonparametric methods is low.

Method

In the study, we proposed an improved MR method combining parametric and nonparametric models based on the previous MR method (Han, JASA 109(507):1159-73, 2014) to improve the robustness to model misspecification and the eff

SUBMITTER: Wang C 

PROVIDER: S-EPMC10568861 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature