Evaluating time-varying treatment effects in hybrid SMART-MRT designs

2026-02-24

AI Summary

This study addresses the challenge of evaluating time-varying synergistic effects of adaptive interventions on both proximal and distal outcomes across multiple timescales (e.g., hours to weeks) in hybrid SMART-MRT experimental designs that integrate human and digital components. It proposes a novel causal inference framework that formally defines the synergistic causal effect of multiscale interventions within such hybrid settings and establishes corresponding identification conditions and estimation strategies. By integrating time-varying treatment effect modeling with analytical techniques tailored for fused SMART-MRT data, the framework was successfully applied in the M-Bridge empirical study to assess the efficacy of a hybrid intervention aimed at reducing binge drinking among college students, thereby demonstrating its validity and practical utility.

Technology Category

• Machine Learning: Causal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Causality

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Recently a new experimental approach, the hybrid experimental design (HED), was introduced to enable investigators to answer scientific questions about building behavioral interventions in which human-delivered and digital components are integrated and adapted on multiple timescales: slow (e.g., every few weeks) and fast (e.g., every few hours), respectively. An increasingly common HED involves the integration of the sequential, multiple assignment, randomized trial (SMART) with the micro-randomized trial (MRT), allowing investigators to answer scientific questions about potential synergistic effects of digital and human-delivered interventions. Approaches to formalize these questions in terms of causal estimands and associated data analytic methods are limited. In this paper, we formally define and assess these synergistic effects in hybrid SMART-MRTs on both proximal and distal outcomes. Practical utility is shown through the analysis of M-Bridge, a hybrid SMART-MRT aimed at reducing binge drinking among first-year college students.

Problem

• hybrid SMART-MRT
• time-varying treatment effects
• synergistic effects
• behavioral interventions
• causal estimands

Innovation

• hybrid SMART-MRT
• time-varying treatment effects
• causal estimands
• micro-randomized trial
• behavioral intervention