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 consistent estimation of the optimal individualized treatment rule. Using techniques from semiparametric efficiency theory, we derive rates of convergence for the proposed estimators and use these rates to characterize the bias-variance trade-off for estimating individualized treatment rules with classification-based methods. Simulation experiments informed by these results demonstrate that it is possible to construct new estimators within the proposed framework that significantly outperform existing ones. We illustrate the proposed methods using data from a labor training program and a study of inflammatory bowel syndrome.

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• Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
• Performance guarantees for individualized treatment rules
• Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes
• Higher Order Estimating Equations for High-dimensional Models

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• Near-optimal Individualized Treatment Recommendations
• A Semiparametric Approach to Model Effect Modification
• Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score
• Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models
• Doubly Robust Direct Learning for Estimating Conditional Average Treatment Effect
• Learning Optimal Distributionally Robust Individualized Treatment Rules