In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of units (e.g., individuals, plots of land, or experiences) is assigned to a treatment (treatment A), and the remainder of the population is assigned to the control treatment (treatment B). The difference in average outcome by treatment group is an estimate of the average effect of the treatment. However, motivating our study, the setting for modern experiments is often different, with the outcomes and treatment assignments indexed by multiple populations. For example, outcomes may be indexed by buyers and sellers, by content creators and subscribers, by drivers and riders, or by travelers and airlines and travel agents, with treatments potentially varying across these indices. Spillovers or interference can arise from interactions between units across populations. For example, sellers' behavior may depend on buyers' treatment assignment, or vice versa. This can invalidate the simple comparison of means as an estimator for the average effect of the treatment in classical RCTs. We propose new experiment designs for settings in which multiple populations interact. We show how these designs allow us to study questions about interference that cannot be answered by classical randomized experiments. Finally, we develop new statistical methods for analyzing these Multiple Randomization Designs.
翻译:在本研究中,我们引入了一个新的实验设计类别。在典型的随机控制试验(RCT)或A/B测试中,随机选择的一组单元(如个人、土地块或经验)被分配到一种治疗(治疗A),其余人口被分配到控制治疗(治疗B)。治疗组的平均结果差异是治疗平均效果的估计。然而,激励我们的研究,现代实验的设置往往不同,由多个人口对结果和治疗任务进行指数化。例如,购买者和销售者、内容创建者和订户、驾驶者和订户、旅行者、航空公司和旅行社对结果进行索引化,处理方法在这些指数之间可能有所不同。斯皮洛夫或干扰可能来自不同人群之间的相互作用。例如,卖方的行为可能取决于买方的治疗任务,反之,这可能会使简单比较方法无法用来作为典型RCT治疗平均效果的估测器。例如,我们提议对多种人口进行新的实验设计环境进行新的实验设计,在这种环境下,许多人口进行新的干预,我们最后能够对这些模型进行分析。我们允许这些设计如何发展这些模型来分析。