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.

翻译：本文研究算法辅助人类决策的最优策略。此类策略是测量预处理变量的决策函数，通过利用自然处理值，享有"超最优性"性质——确保其性能优于传统最优策略。当存在未测量混杂因素时，采用超最优策略可带来显著收益；当不存在未测量混杂因素时，超最优策略与传统最优策略一致。此外，在非实验研究中识别超最优策略下的预期结果，所需假设与二元处理场景下传统最优策略价值函数的识别假设相同。为阐明超最优策略的实用性，我们在常见的工具变量设定中推导了新的识别与估计结果，并基于这些推导分析了最优策略文献中的典型案例，包括即时重症监护治疗对生存率影响的案例研究。