论文标题
讨论Kallus（2020）和Mo，Qi和Liu（2020）：政策学习的新目标
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning
论文作者
论文摘要
我们讨论了内森·卡鲁斯（Nathan Kallus）通过“最佳重新定位”和“学习最佳分配具有强大的个性化治疗规则”，由Weibin Mo，Zhengling Qi和Yufeng Liu提出了发人深省的政策学习新目标功能。我们表明，在重新定位框架内工作时，将价值函数的曲率考虑在内很重要，我们介绍了两种方法。我们还描述了在学习分配稳健策略时利用校准数据的更有效的方法。
We discuss the thought-provoking new objective functions for policy learning that were proposed in "More efficient policy learning via optimal retargeting" by Nathan Kallus and "Learning optimal distributionally robust individualized treatment rules" by Weibin Mo, Zhengling Qi, and Yufeng Liu. We show that it is important to take the curvature of the value function into account when working within the retargeting framework, and we introduce two ways to do so. We also describe more efficient approaches for leveraging calibration data when learning distributionally robust policies.
