Micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the eﬀectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. The MRT context has motivated a new class of causal estimands, termed “causal excursion effects”, for which inference can be made by a weighted, centered least squares approach (Boruvka et al., 2017). Existing methods assume between-subject independence and non-interference. Deviations from these assumptions often occur which, if unaccounted for, may result in bias and overconﬁdent variance estimates. In this paper, causal excursion eﬀects are considered under potential cluster-level correlation and interference and when the treatment eﬀect of interest depends on cluster-level moderators. The utility of our proposed methods is shown by analyzing data from a multi-institution cohort of ﬁrst year medical residents in the United States. The approach paves the way for construction of mHealth interventions that account for observed social network information.
Keywords Causal Inference; Clustered Data; Just-In-Time Adaptive Interventions; Microrandomized Trials; Mobile Health; Moderation Effect