  Individualized treatment rules (ITRs) have been widely applied in many fields
such as precision medicine and personalized marketing. Beyond the extensive
studies on ITR for binary or multiple treatments, there is considerable
interest in applying combination treatments. This paper introduces a novel ITR
estimation method for combination treatments incorporating interaction effects
among treatments. Specifically, we propose the generalized $\psi$-loss as a
non-convex surrogate in the residual weighted learning framework, offering
desirable statistical and computational properties. Statistically, the
minimizer of the proposed surrogate loss is Fisher-consistent with the optimal
decision rules, incorporating interaction effects at any intensity level - a
significant improvement over existing methods. Computationally, the proposed
method applies the difference-of-convex algorithm for efficient computation.
Through simulation studies and real-world data applications, we demonstrate the
superior performance of the proposed method in recommending combination
treatments.
