论文标题
平衡且健壮的随机治疗作业：健康保险实验的有限选择模型
Balanced and Robust Randomized Treatment Assignments: The Finite Selection Model for the Health Insurance Experiment and Beyond
论文作者
论文摘要
卡尔·莫里斯（Carl Morris）在1970年代开发了有限选择模型（FSM），用于设计兰德健康保险实验（HIE）（HIE）（Morris 1979，Newhouse等，1993），美国在美国进行的最大，最全面的社会科学实验之一，FSM背后的想法是，每个治疗小组都可以选择一个公平的秩序，以公平的秩序为单位，以公平的秩序为单位。在每个回合中，一个治疗组都选择可用的单元，该单元在标准方面最大程度地提高了其所得单元组的组合质量。在Hie及其他地区，我们重新访问，正式化并扩展了FSM作为实验设计的一般工具。 利用D-典型性的思想，我们提出和分析了FSM中的新选择标准。使用D-最佳选择函数的FSM没有调谐参数，是仿射不变的，并且在适当的情况下检索几种经典设计，例如随机块和匹配的对配对设计。对于多ARM实验，我们提出算法来生成公平和随机的治疗顺序。我们在基于HIE和健康和社会科学的十项随机研究中证明了FSM在案例研究中的表现。在一项典型的研究中，FSM平均比完全随机分配的协变量平衡高68％，协变量平衡要比重新汇总分解。我们建议在实验设计中考虑FSM的概念简单，效率和鲁棒性。
The Finite Selection Model (FSM) was developed by Carl Morris in the 1970s for the design of the RAND Health Insurance Experiment (HIE) (Morris 1979, Newhouse et al. 1993), one of the largest and most comprehensive social science experiments conducted in the U.S. The idea behind the FSM is that each treatment group takes its turns selecting units in a fair and random order to optimize a common assignment criterion. At each of its turns, a treatment group selects the available unit that maximally improves the combined quality of its resulting group of units in terms of the criterion. In the HIE and beyond, we revisit, formalize, and extend the FSM as a general tool for experimental design. Leveraging the idea of D-optimality, we propose and analyze a new selection criterion in the FSM. The FSM using the D-optimal selection function has no tuning parameters, is affine invariant, and when appropriate, retrieves several classical designs such as randomized block and matched-pair designs. For multi-arm experiments, we propose algorithms to generate a fair and random selection order of treatments. We demonstrate FSM's performance in a case study based on the HIE and in ten randomized studies from the health and social sciences. On average, the FSM achieves 68% better covariate balance than complete randomization and 56% better covariate balance than rerandomization in a typical study. We recommend the FSM be considered in experimental design for its conceptual simplicity, efficiency, and robustness.
