Rejoinder: New Objectives for Policy Learning
Nathan Kallus
TL;DR
This paper offers a response to discussions on a new approach for more efficient policy learning, emphasizing the importance of optimal retargeting in statistical policy methods.
Contribution
It clarifies and defends the proposed objectives for policy learning, highlighting their potential to improve efficiency and effectiveness.
Findings
Emphasizes the benefits of optimal retargeting in policy learning
Addresses critiques and clarifies methodological choices
Highlights potential improvements in policy learning efficiency
Abstract
I provide a rejoinder for discussion of "More Efficient Policy Learning via Optimal Retargeting" to appear in the Journal of the American Statistical Association with discussion by Oliver Dukes and Stijn Vansteelandt; Sijia Li, Xiudi Li, and Alex Luedtkeand; and Muxuan Liang and Yingqi Zhao.
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Taxonomy
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