The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt one of two or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this paper, we propose a novel Double Encoder Model (DEM) to estimate the individualized treatment rule for combination treatments. The proposed double encoder model is a nonparametric model which not only flexibly incorporates complex treatment effects and interaction effects among treatments, but also improves estimation efficiency via the parameter-sharing feature. In addition, we tailor the estimated ITR to budget constraints through a multi-choice knapsack formulation, which enhances our proposed method under restricted-resource scenarios. In theory, we provide the value reduction bound with or without budget constraints, and an improved convergence rate with respect to the number of treatments under the DEM. Our simulation studies show that the proposed method outperforms the existing ITR estimation in various settings. We also demonstrate the superior performance of the proposed method in a real data application that recommends optimal combination treatments for Type-2 diabetes patients.

翻译：个性化治疗方案（ITR）根据个体特征推荐最优治疗策略，在精准医疗、个性化教育及个性化营销等领域备受关注。现有ITR估计方法主要涉及两种或多种治疗的单一选择，而多治疗联合方案在诸多领域可能更具效力。本文提出一种新型双编码器模型（DEM）用于联合治疗的个性化规则估计。该非参数模型不仅能灵活刻画复杂的治疗效应及治疗间交互作用，更通过参数共享特性提升估计效率。在资源配置受限场景下，我们进一步借助多选背包问题建模，使估计得到的ITR适配预算约束条件。理论上，我们推导了有无预算约束下的价值损失界，并给出DEM模型下随治疗数量提升的收敛速率改进证明。仿真研究表明，本方法在多种场景下均优于现有ITR估计方法。针对2型糖尿病患者的真实数据应用亦证实，该方法在推荐最优联合治疗方案时具有显著性能优势。