More Efficient Policy Learning via Optimal Retargeting
通过最优目标人群重设提高策略学习效率AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究观察性数据中的个性化处理规则、动作重叠和策略价值评估，核心问题属于因果策略学习。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
通过最优目标人群重设提高策略学习效率
- 研究问题
- 观察性数据中不同动作的重叠不足时，能否通过改变策略优化所针对的人群，降低评估方差并改善学得的个性化策略？
- 方法与思路
- 作者以新学习目标的渐近有效估计方差为标准，刻画二元和多动作下的最优参考策略及重设权重，并考虑额外的偏差控制。
- 材料陈述的贡献
- 摘要称目标人群重设在总体层面可能引入很少或不引入偏差，模拟与个性化就业咨询案例显示其可改善策略学习表现。
- 阅读关注点
- 应核对重设后价值函数与原目标的关系、低偏差结论所需条件，以及最优权重如何平衡重叠不足、估计方差和目标偏离。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明，不能推断刊本变化；材料混入出版元数据，但研究部分仍为摘要，不能据此确认普遍改善。
原文摘要
Nathan Kallus* School of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York, NYColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Nathan Kallus kallus@cornell.edu School of Operations Research and Information Engineering and Cornell Tech, Cornell University, 2 West Loop Rd, New York, NY 10044.AbstractPolicy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned policies. We study a solution to this problem based on retargeting, that is, changing the population on which policies are optimized. We first argue that at the population level, retargeting may induce little to no bias. We then characterize the optimal reference policy and retargeting weights in both binary-action and multi-action settings. We do this in terms of the asymptotic efficient estimation variance of the new learning objective. We further consider weights that additionally control for potential bias due to retargeting. Extensive empirical results in a simulation study and a case study of personalized job counseling demonstrate that retargeting is a fairly easy way to significantly improve any policy learning procedure applied to observational data. Supplementary materials for this article are available online.