论文标题
机器预测和人力决策，收益和技能的差异
Machine Predictions and Human Decisions with Variation in Payoffs and Skill
论文作者
论文摘要
由于激励措施和可用信息的差异，人类决策的不同。对于评估机器学习预测是否可以改善决策结果的评估，这是一个重大挑战。我们提出了一个框架，该框架将大规模数据上的机器学习纳入了一个选择模型，该模型具有决策者收益功能和预测技能的异质性。我们将此框架应用于改善初级保健抗生素处方效率的主要健康政策问题，这是抗生素耐药性的主要原因之一。我们的分析揭示了医生诊断细菌感染的技能以及医师如何与抗生素治疗益处固有的外部性差异。反事实策略模拟表明，与估计的医师以及保守性社会计划者的替代政策相比，机器学习预测与医师诊断技能的结合降低了25.4％，并取得了最大的福利收益，并取得了最大的福利收益。
Human decision-making differs due to variation in both incentives and available information. This constitutes a substantial challenge for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply this framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians' skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show that the combination of machine learning predictions with physician diagnostic skill results in a 25.4 percent reduction in prescribing and achieves the largest welfare gains compared to alternative policies for both estimated physician as well as conservative social planner preference weights on the antibiotic resistance externality.
