Assessing time-varying causal effect moderation in the presence of cluster-level treatment effect heterogeneity and interference
本文扩展了微随机试验中的因果偏移效应定义，允许集群级处理效应异质性和干扰，并用美国医学实习生数据展示了方法实用性。
Summary The micro-randomized trial is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health intervention components that may be delivered at hundreds or thousands of decision points. Micro-randomized trials have motivated a new class of causal estimands, termed causal excursion effects, for which semiparametric inference can be conducted via a weighted, centred least-squares criterion (Boruvka et al., 2018). Causal excursion effects allow health scientists to answer important scientific questions about how intervention effectiveness may change over time or may be moderated by individual characteristics, time-varying context or past responses. Existing definitions and associated methods assume between-subject independence and noninterference. Deviations from these assumptions often occur. In this paper, causal excursion effects are revisited under potential cluster-level treatment effect heterogeneity and interference, where the treatment effect of interest may depend on cluster-level moderators. Utility of the proposed methods is shown by analysing data from a multi-institution cohort of first-year medical residents in the United States.