DESIGNS FOR ESTIMATING THE TREATMENT EFFECT IN NETWORKS WITH INTERFERENCE

• 成果类型: Article
• 署名作者: Jagadeesan, Ravi; Pillai, Natesh S.; Volfovsky, Alexander
• 署名单位: Harvard University; Harvard University; Duke University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/18-AOS1807
• 发表日期: 2020
• 页码: 679-712
• 关键词: Causal Inference

摘要

In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment assignment as a quasi-coloring on a graph. Our idea of a perfect quasi-coloring strives to match every treated unit on a given network with a distinct control unit that has identical number of treated and control neighbors. For a wide range of interference functions encountered in applications, we show both by theory and simulations that the classical Neymanian estimator for the direct effect has desirable properties for our designs.