Causal Spillover Effects Using Instrumental Variables

• 成果类型: Article
• 署名作者: Vazquez-Bare, Gonzalo
• 署名单位: University of California System; University of California Santa Barbara
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.2021920
• 发表日期: 2023
• 页码: 1911-1922
• 关键词: identification; inference; assignment

摘要

I set up a potential outcomes framework to analyze spillover effects using instrumental variables. I characterize the population compliance types in a setting in which spillovers can occur on both treatment take-up and outcomes, and provide conditions for identification of the marginal distribution of compliance types. I show that intention-to-treat (ITT) parameters aggregate multiple direct and spillover effects for different compliance types, and hence do not have a clear link to causally interpretable parameters. Moreover, rescaling ITT parameters by first-stage estimands generally recovers a weighted combination of average effects where the sum of weights is larger than one. I then analyze identification of causal direct and spillover effects under one-sided noncompliance, and show that causal effects can be estimated by 2SLS in this case. I illustrate the proposed methods using data from an experiment on social interactions and voting behavior. I also introduce an alternative assumption, independence of the peers' types, that identifies parameters of interest under two-sided noncompliance by restricting the amount of heterogeneity in average potential outcomes. Supplementary material of this article will be available in online.