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B0560
Title: Network interference in micro-randomized trials Authors:  Shuangning Li - Harvard University (United States) [presenting]
Stefan Wager - Stanford University (United States)
Abstract: 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 smartphones 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. 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.