Cluster-Randomized Trials with Cross-Cluster Interference

• 成果类型: Article
• 署名作者: Leung, Michael P.
• 署名单位: University of California System; University of California Santa Cruz
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2566416
• 发表日期: 2026-04-03
• 页码: 1361-1371
• 关键词: Causal Inference; Cluster-randomized trials; Experimental design; interference; vaccine

摘要

The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This article develops methods for reducing bias due to cross-cluster interference. We first propose an estimation strategy that excludes units not surrounded by clusters assigned to the same treatment arm. We show that this substantially reduces bias relative to conventional difference-in-means estimators without significant cost to variance. Second, we formally establish a bias-variance tradeoff in the choice of clusters: constructing fewer, larger clusters reduces bias due to interference but increases variance. We provide a rule for choosing the number of clusters to balance the asymptotic orders of the bias and variance of our estimator. Finally, we consider unsupervised learning for cluster construction and provide theoretical guarantees for k-medoids. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.