The micro-randomized trial (MRT) is an experimental design that can be used to develop optimal mobile health interventions. In MRTs, interventions in the form of notifications or messages are sent through smart phones to individuals, targeting a health-related outcome such as physical activity or weight management. Often, mobile health interventions have a social media component; an individual's outcome could thus depend on other individuals' treatments and outcomes. In this paper, we study the micro-randomized trial in the presence of such cross-unit interference. We model the cross-unit interference with a network interference model; the outcome of one individual may affect the outcome of another individual if and only if they are connected by an edge in the network. Assuming the dynamics can be represented as a Markov decision process, we analyze the behavior of the outcomes in large sample asymptotics and show that they converge to a mean-field limit when the sample size goes to infinity. Based on the mean-field result, we give characterization results and estimation strategies for various causal estimands including the short-term direct effect of a binary intervention, its long-term direct effect and its long-term total effect.
翻译:微随机试验(MRT)是一种实验性设计,可用于发展最佳的移动保健干预措施。在MRTs中,以通知或信息形式采取的干预措施通过智能电话向个人发送,针对与健康有关的结果,如身体活动或体重管理等。移动保健干预措施通常具有社交媒体成分;因此,个人的结果可能取决于其他个人的治疗和结果。在本文中,我们研究在出现跨单位干扰的情况下的微随机试验。我们模拟跨单位干扰网络干扰模式的跨单位干扰;一个人的结果,如果并且只有在他们与网络的边缘相连时,才会影响另一个人的结果。假设动态可以作为Markov的决策过程来体现,我们分析大样本中的随机作用,并表明当样本大小达到一定的平均值时,这些结果会合在一起。根据中位结果,我们为各种因果关系估计结果和估计战略,包括二进干预的短期直接效果、其长期影响及其长期影响。