Optimal regimes for algorithm-assisted human decision-making
研究了算法辅助人类决策的最优机制，提出一种超优机制，在存在未测量混杂时优于传统最优机制，并在工具变量设定下给出识别与估计方法。
Summary 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 nonexperimental 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 identification and estimation results in a common instrumental variable setting. We use these derivations to analyse examples from the optimal regimes literature, including a case study of the effect of prompt intensive care treatment on survival.