Discussion of Kallus (2020) and Mo et al. (2020)
成果类型：
Editorial Material
署名作者：
Liang, Muxuan; Zhao, Ying-Qi
署名单位：
Fred Hutchinson Cancer Center
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2020.1833887
发表日期：
2021
页码：
690-693
关键词：
摘要：
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. for this article are available online.
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