Personalized Dose Finding Using Outcome Weighted Learning Comment

• 成果类型: Editorial Material
• 署名作者: Qian, Min
• 署名单位: Columbia University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1243479
• 发表日期: 2016
• 页码: 1538-1541
• 关键词: INDIVIDUALIZED TREATMENT RULES

摘要

This comment deals with issues related to the article by Chen, Zeng, and Kosorok. We present several potential modifications of the outcome weighted learning approach.Those modifications are basecIon truncated l(2) loss. One advantage of l(2) loss is that it is differentiable everywhere, which makes it more stable and computationally more tractable.