算法辅助人类决策的最优策略
Optimal regimes for algorithm-assisted human decision-making
AI总结：
本文提出算法辅助人类决策的超最优策略，利用自然治疗值保证优于传统最优策略，并在工具变量设置下给出识别与估计方法，通过重症监护案例验证其效用。
摘要：
我们考虑算法辅助人类决策的最优策略。此类策略是已测量的治疗前变量的决策函数，并通过利用自然治疗值，具有“超最优性”性质，从而保证优于传统最优策略。当存在未测量的混杂因素时，使用超最优策略的收益可能相当可观。当不存在未测量的混杂因素时，超最优策略与传统最优策略相同。此外，在非实验研究中识别超最优策略下的期望结果，在治疗为二值时所需的假设与识别传统最优策略下价值函数的假设相同。为说明超最优策略的效用，我们在常见的工具变量设置中推导出新的识别与估计结果。我们利用这些推导分析了最优策略文献中的示例，包括一项关于及时重症监护治疗对生存影响的案例研究。
英文摘要：
We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a "superoptimality" property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal regimes. Furthermore, identification of the expected outcome under superoptimal regimes in non-experimental studies requires the same assumptions as identification of value functions under conventional optimal regimes when the treatment is binary. To illustrate the utility of superoptimal regimes, we derive new identification and estimation results in a common instrumental variable setting. We use these derivations to analyze examples from the optimal regimes literature, including a case study of the effect of prompt intensive care treatment on survival.
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