We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. We demonstrate the approach by proposing an asymptotically valid test for the hypothesis of policy irrelevance/no treatment effects and bounding the mean-squared error of a k-nearest-neighbor estimator for the average or distributional policy effect/treatment response.

翻译：我们提出了一种新的非参数建模框架，用于在结果依赖于社会或经济网络中个体连接方式时的因果推断。这种网络干扰描述涉及处理溢出、社会互动、社会学习、信息扩散、疾病与金融传染、社会资本形成等多个领域的广泛文献。我们的方法首先通过基于路径距离测量的其他个体及连接的配置来刻画个体在网络中的连接方式，随后通过汇集相似配置个体的结果数据来学习政策或处理分配的影响。我们通过提出一个策略无关性/无处理效应的渐近有效检验，并限制用于平均或分布政策效应/处理响应的k近邻估计量的均方误差，来验证该方法的有效性。