Individualized treatment rules (ITRs) formalize precision medicine by assigning treatments according to patient covariates, with the goal of maximizing expected clinical outcomes. Such rules are especially important when treatment effects vary across patients, as in chronic diseases where demographic, clinical, genetic, imaging, or biomarker information may modify the relative benefits of available therapies. Individualized treatment rules (ITRs) formalize precision medicine by assigning treatments according to patient covariates, with the goal of maximizing expected clinical outcomes. Such rules are especially important when treatment effects vary across patients, as in chronic diseases where demographic, clinical, genetic, imaging, or biomarker information may modify the relative benefits of available therapies. Outcome weighted learning (OWL) estimates ITRs by recasting treatment assignment as a weighted classification problem that directly targets clinical value. Motivated by the flexibility of modern neural networks, we extend single hidden-layer neural-network OWL (NNOWL) from ridge-type regularization to nonlinear variable selection and kernel-based approximation. We establish non-asymptotic convergence rates for these estimators, and study the global convergence and implicit bias of gradient descent for NNOWL. Finally, we extend the neural-network methods from OWL to residual weighted learning. Simulation studies illustrate the roles of over-parameterization, kernel approximation, and nonlinear variable selection, and a data application in Alzheimer's disease demonstrates the proposed methods.

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