Efficient augmentation and relaxation learning for individualized treatment rules using observational data.

ABSTRACT: Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. A globally aging population, rising healthcare costs, and increased access to patient-level data have created an urgent need for high-quality estimators of individualized treatment rules that can be applied to observational data. A recent and promising line of research for estimating individualized treatment rules recasts the problem of estimating an optimal treatment rule as a weighted classification problem. We consider a class of estimators for optimal treatment rules that are analogous to convex large-margin classifiers. The proposed class applies to observational data and is doubly-robust in the sense that correct specification of either a propensity or outcome model leads to consi

SUBMITTER: Zhao YQ 

PROVIDER: S-EPMC6705615 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature