Abstract

We discuss the results on improving the generalizability of individualized treatment rule following the work by Kallus and Mo et al. We note that the advocated weights in the work of Kallus are connected to the efficient score of the contrast function. We further propose a likelihood-ratio-based method (LR-ITR) to accommodate covariate shifts, and compare it to the CTE-DR-ITR method proposed by Mo et al. We provide the upper-bound on the risk function of the target population when both the covariate shift and the contrast function shift are present. Numerical studies show that LR-ITR can outperform CTE-DR-ITR when there is only covariate shift. Supplementary materials for this article are available online.

 抽象的

我们讨论了继 Kallus 和 Mo 等人的工作后提高个体化治疗规则的普遍性的结果。我们注意到，Kallus 工作中提倡的权重与对比函数的有效得分相关。我们进一步提出了一种基于似然比的方法（LR-ITR）来适应协变量偏移，并将其与 Mo 等人提出的 CTE-DR-ITR 方法进行比较。当协变量偏移和对比函数偏移同时存在时，我们提供了目标人群风险函数的上限。数值研究表明，当仅存在协变量偏移时，LR-ITR 的性能优于 CTE-DR-ITR。本文的补充材料可在线获取。