Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding
针对观察性研究中因未测量网络混杂（如同质性偏差和情境混杂）导致的因果同伴效应识别难题，提出利用双重阴性对照变量进行非参数识别，并给出广义矩估计方法及其一致性、渐近正态性和方差估计。
Abstract 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 is network dependence of observations. We establish a framework that leverages a pair of negative control outcome and exposure variables (double negative controls) to non-parametrically identify causal peer effects in the presence of unmeasured network confounding. We then propose a generalised method of moments estimator and establish its consistency and asymptotic normality under an assumption about ψ-network dependence. Finally, we provide a consistent variance estimator.