论文标题
算法辅助人类决策的最佳制度
Optimal regimes for algorithm-assisted human decision-making
论文作者
论文摘要
我们考虑算法辅助人类决策的最佳制度。这样的制度是测量的预处理变量的决策功能，并且通过利用自然处理值，可以享受“超级小”属性，从而保证它们表现优于常规的最佳制度。当有无法衡量的混杂时，使用超级临时制度的好处可能是相当大的。当没有无法衡量的混杂状态时，超级最佳制度与常规最佳制度相同。此外，在非实验研究中对超级最佳制度下的预期结果的鉴定需要与在治疗是二进制时在常规最佳制度下对价值功能的识别相同的假设。为了说明超级最佳制度的实用性，我们得出了新的识别和估计导致通用仪器变量设置。我们使用这些派生来分析最佳政权文献中的示例，包括对迅速重症监护对生存的影响的案例研究。
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.
