发表机构
Brown University（布朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对网络数据中匹配可能引发虚假关联的问题，提出带网络邻近性约束的匹配方法和基于随机化的推断，并在R包netmatchRI中实现，应用于弗雷明汉心脏研究。
AI 中文摘要
匹配方法被广泛用于通过形成匹配集来模拟随机实验，在匹配集中，处理组和对照组单位在观测协变量上仅存在随机差异。然而，当研究总体由来自单一网络或少量网络的相互连接的单位组成时，仅基于观测协变量进行匹配可能产生在网络上比随机情况下更紧密相连的匹配单位。这种匹配集内网络邻近性的增加，当处理变量和结果变量在网络上表现出相似的自相关模式时，可能诱发处理与结果之间的虚假关联。为了在保持因果比较有效性的同时减少虚假关联，我们提出了一种新的匹配方法，该方法在匹配协变量相似的单位时附加网络邻近性约束。对于匹配后的推断，我们提出了一种基于随机化的程序，用于检验无因果效应的尖锐零假设。该推断使用渐近正态近似，并适应从每个匹配集获得的检验统计量之间的统计依赖性，而无需明确指定其相关结构。我们通过模拟研究证明了所提出方法的有效性和实用性，并将其应用于弗雷明汉心脏研究。匹配方法和后续推断程序已在R包netmatchRI中实现。
英文摘要
Matching is widely used to mimic randomized experiments by forming matched sets in which treated and control units differ only randomly with respect to observed covariates. However, when the study population consists of interconnected units from a single network or a small number of networks, matching solely on observed covariates may produce matched units that are more closely connected in the network than would occur by chance. This increased network proximity within matched sets can induce spurious associations between treatment and outcome when both variables exhibit similar autocorrelation patterns on the network. To reduce spurious associations while preserving the validity of causal comparisons, we propose a new matching method that matches units with similar covariates subject to additional network-proximity constraints. For post-matching inference, we propose a randomization-based procedure for testing the sharp null hypothesis of no causal effect. The inference uses the asymptotic normal approximation and accommodates statistical dependence among test statistics obtained from each matched set without requiring explicit specifications of their correlation structures. We demonstrate the validity and utility of the proposed methods through simulation studies and apply them to the Framingham Heart Study. The matching method and subsequent inference procedure are implemented in the R package netmatchRI.
