Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity

• Activity Number: 398 - Recent Advances in Streaming Data Analytics
• Type: Invited
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Walter Dempsey* and Zhenke Wu and Jieru Shi
• Companies: University of Michigan and University of Michigan, Ann Arbor and University of Michigan
• Keywords: causal inference; clustered data; just-in-time adaptive interventions; micro-randomized trials; mobile health; moderation effect

Abstract

The 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. MRTs have motivated a new class of causal estimands, termed “causal excursion effects”, for which semiparametric inference can be conducted via a weighted, centered least squares criterion (Boruvka et al.,2018). Existing methods assume between-subject independence and non-interference. 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 analyzing data from a multi-institution cohort of first year medical residents in the United States.