Identification and Estimation of Causal Peer Effects Using Double Negative Controls for Unmeasured Network Confounding
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利用双重负对照识别与估计未观测网络混杂下的因果同伴效应
- 研究问题
- 观察性网络数据存在同质性选择、情境混杂和观测间依赖时，如何识别同伴行为的因果效应并进行有效统计推断？
- 方法与思路
- 作者结合负对照结果与负对照暴露建立非参数识别框架，提出 GMM 估计，并在 ψ 网络依赖假设下构造渐近推断。
- 材料陈述的贡献
- 摘要称双重负对照可用于识别因果同伴效应，建立估计量一致性与渐近正态性，并提供网络异方差自相关稳健方差估计。
- 阅读关注点
- 核对负对照变量的有效性与完整识别条件、GMM 矩条件、网络依赖限制，以及教育应用中的变量选择和方差估计。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出完整识别假设，不能认为任意一对安慰剂变量都能消除网络混杂。
原文摘要
Scientists have been interested in estimating causal peer effects to understand how people's behaviors are affected by their network peers. However, it is well known that identification and estimation of causal peer effects are challenging in observational studies for two reasons. The first is the identification challenge due to unmeasured network confounding, for example, homophily bias and contextual confounding. The second issue is network dependence of observations, which one must take into account for valid statistical inference. Negative control variables, also known as placebo variables, have been widely used in observational studies including peer effect analysis over networks, although they have been used primarily for bias detection. In this article, we establish a formal framework which leverages a pair of negative control outcome and exposure variables (double negative controls) to nonparametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalized method of moments estimator for causal peer effects, and establish its consistency and asymptotic normality under an assumption about $ψ$-network dependence. Finally, we provide a network heteroskedasticity and autocorrelation consistent variance estimator. Our methods are illustrated with an application to peer effects in education.