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Activity Number: 522 - Life Science Applications of Data Science
Type: Contributed
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #321000
Title: Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity
Author(s): Jieru Shi* and Zhenke Wu and Walter Dempsey
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. The utility of the proposed methods is shown by analyzing data from a multi-institution cohort of first-year medical residents in the United States.


Authors who are presenting talks have a * after their name.

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