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Individualized treatment rules aim to identify if, when, which, and to whomtreatment should be applied. A globally aging population, rising healthcarecosts, and increased access to patient-level data have created an urgent needfor high-quality estimators of individualized treatment rules that can beapplied to observational data. A recent and promising line of research forestimating individualized treatment rules recasts the problem of estimating anoptimal treatment rule as a weighted classification problem. We consider aclass of estimators for optimal treatment rules that are analogous to convexlarge-margin classifiers. The proposed class applies to observational data andis doubly-robust in the sense that correct specification of either a propensityor outcome model leads to consistent estimation of the optimal individualizedtreatment rule. Using techniques from semiparametric efficiency theory, wederive rates of convergence for the proposed estimators and use these rates tocharacterize the bias-variance trade-off for estimating individualizedtreatment rules with classification-based methods. Simulation experimentsinformed by these results demonstrate that it is possible to construct newestimators within the proposed framework that significantly outperform existingones. We illustrate the proposed methods using data from a labor trainingprogram and a study of inflammatory bowel syndrome.

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