作为实验的算法：机器学习、市场设计与政策资格规则
Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
AI总结：
本文提出以算法决策为工具变量的因果效应估计量，并用于评估 CARES Act 对医院的数百亿美元救济，发现其对 COVID-19 相关医院活动影响甚微，而朴素估计存在选择偏差。
摘要：
算法在政策和商业决策中所占的比例日益增长。我们针对一类随机和确定性算法，开发了一种将算法决策作为工具变量的处理效应估计量。对于定义明确的因果效应，该估计量具有一致性和渐近正态性。我们框架的一个特例是具有复杂边界的多维回归断点设计。我们应用该估计量评估《冠状病毒援助、救济和经济安全法案》（CARES Act），该法案通过一项算法规则向医院分配了价值数百亿美元的救济资金。结果表明，该资金对与 COVID-19 相关的医院活动几乎没有影响。朴素估计则表现出选择偏差。
英文摘要：
Algorithms make a growing portion of policy and business decisions. We develop a treatment-effect estimator using algorithmic decisions as instruments for a class of stochastic and deterministic algorithms. Our estimator is consistent and asymptotically normal for well-defined causal effects. A special case of our setup is multidimensional regression discontinuity designs with complex boundaries. We apply our estimator to evaluate the Coronavirus Aid, Relief, and Economic Security Act, which allocated many billions of dollars worth of relief funding to hospitals via an algorithmic rule. The funding is shown to have little effect on COVID-19-related hospital activities. Naive estimates exhibit selection bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
