Abstract
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 ψ-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.
Funding Statement
This work is supported by National Science Foundation Grants DMS 2210640 and DMS 1952406.
Acknowledgments
The authors would like to thank the anonymous referees, the Associate Editor and the Editor for their constructive comments that improved the quality of this paper.
Citation
Qi Xu. Xiaoke Cao. Geping Chen. Hanqi Zeng. Haoda Fu. Annie Qu. "Multi-label residual weighted learning for individualized combination treatment rule." Electron. J. Statist. 18 (1) 1517 - 1548, 2024. https://doi.org/10.1214/24-EJS2236
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