We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate identification conditions for the intention-to-treat effects and the average treatment effects for compliers, while explicitly considering the possibility of misspecification of exposure mapping. Based on our identification results, we develop nonparametric estimation procedures via inverse probability weighting. Their asymptotic properties, including consistency and asymptotic normality, are investigated using an approximate neighborhood interference framework. For an empirical illustration, we apply our method to experimental data on the anti-conflict intervention school program. The proposed methods are readily available with the companion R package latenetwork.

翻译：摘要：本文考虑个体在社会网络中交互且可能未依从分配干预的因果推断模型。特别地，我们假设网络干扰的形式对研究者是未知的。为了在此情形下估计有意义的因果参数，我们引入了一种新的暴露映射概念，该概念可将潜在的复杂溢出效应归纳为工具变量的固定维度统计量。我们研究了意向治疗效应和依从者平均处理效应的识别条件，同时明确考虑了暴露映射误设的可能性。基于识别结果，我们通过逆概率加权构建了非参数估计程序。利用近似邻域干扰框架，我们研究了其渐近性质（包括一致性和渐近正态性）。为进行实证说明，我们将所提方法应用于反冲突干预学校项目的实验数据。所提方法可通过配套的R包latenetwork直接实现。