Multi-Label Residual Weighted Learning for Individualized Combination Treatment Rule

TL;DR

This paper presents a new method for estimating individualized treatment rules for combination treatments, effectively capturing interaction effects and demonstrating superior performance through simulations and real data.

Contribution

It introduces a generalized $ ext{ extbackslash psi}$-loss within residual weighted learning, enabling Fisher-consistent estimation of optimal rules with interaction effects.

Findings

Outperforms existing methods in simulation studies

Effectively captures interaction effects among treatments

Demonstrates superior real-world application results

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 $\psi$-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.