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：我们考虑一个因果推断模型，其中个体在社会网络中相互影响，且可能未遵循分配的处理方案。特别地，我们假设研究者对网络干扰的形式未知。为在此情境下估计有意义的因果参数，我们引入了一个新的“暴露映射”概念，该概念将潜在复杂的溢出效应总结为工具变量的固定维度统计量。我们研究了意向性治疗效应与依从者的平均处理效应的识别条件，同时明确考虑暴露映射可能存在的设定错误。基于识别结果，我们通过逆概率加权开发了非参数估计程序。其渐近性质，包括一致性与渐近正态性，在近似邻域干扰框架下得到验证。为进行实证说明，我们将该方法应用于一项反冲突干预学校项目的实验数据。所提方法可通过配套R包latenetwork直接实现。