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

TL;DR

This paper develops methods to assess how causal effects of mobile health interventions vary over time and across clusters, accounting for heterogeneity and interference, with application to medical resident data.

Contribution

It introduces new causal inference techniques for cluster-level heterogeneity and interference in micro-randomized trials, extending existing methods.

Findings

Methods successfully applied to medical resident data

Revealed significant cluster-level treatment effect heterogeneity

Enhanced understanding of intervention effects over time

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.