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：个体化治疗规则（ITR）根据个体特征推荐最优治疗方案，在精准医疗、个性化教育和个性化营销等领域引起了广泛关注。现有ITR估计方法主要采用两种或多种治疗中的一种。然而，在多个领域中，多种治疗的组合可能更为有效。本文提出了一种新型双编码器模型（DEM）来估计组合治疗的个体化治疗规则。所提出的双编码器模型是一种非参数模型，不仅灵活地纳入了复杂的治疗效应及治疗间的交互效应，还通过参数共享特性提高了估计效率。此外，我们通过多选择背包规划将估计的ITR适配至预算约束，增强了所提方法在资源受限场景下的适用性。理论上，我们给出了有/无预算约束下的价值缩减界，并证明了DEM下关于治疗数量的改进收敛速率。仿真研究表明，所提方法在多种设置下优于现有ITR估计方法。在针对2型糖尿病患者推荐最优组合治疗的真实数据应用中，我们也验证了所提方法的优越性能。