Associate professor in the Department of Public Policy at the University of North Carolina at Chapel Hill.
Undergraduate student majoring in public policy and global studies at the University of North Carolina at Chapel Hill.
Ethics Hum Res. 2023 Jan;45(1):15-28. doi: 10.1002/eahr.500153.
Government agencies and nonprofit organizations have increasingly turned to randomized controlled trials (RCTs) to evaluate public policy interventions. Random assignment is widely understood to be fair when there is equipoise; however, some scholars and practitioners argue that random assignment is also permissible when an intervention is reasonably expected to be superior to other trial arms. For example, some argue that random assignment to such an intervention is fair when the intervention is scarce, for it is sometimes fair to use a lottery to allocate scarce goods. We investigate the permissibility of randomization in public policy RCTs when there is no equipoise, identifying two sets of conditions under which it is fair to allocate access to a superior intervention via random assignment. We also reject oft-made claims that alternative study designs, including stepped-wedge designs and uneven randomization, offer fair ways to allocate beneficial interventions.
政府机构和非营利组织越来越多地转向随机对照试验（RCT）来评估公共政策干预措施。当存在均衡时，随机分配被广泛认为是公平的；然而，一些学者和实践者认为，当干预措施合理预期优于其他试验组时，随机分配也是可以接受的。例如，一些人认为，当干预措施稀缺时，将其随机分配是公平的，因为有时使用抽签来分配稀缺物品是公平的。我们研究了当不存在均衡时，公共政策 RCT 中随机化的可接受性，确定了在两种情况下，通过随机分配公平地分配对优越干预措施的使用权是公平的。我们还驳斥了常见的说法，即替代研究设计，包括逐步楔形设计和不均匀随机化，提供了公平的方法来分配有益的干预措施。
