中文摘要
我们提出了一个新的非参数建模框架, 适用于结果取决于个体在社会或经济网络中关联方式的情形下的因果推断。此类网络干扰涵盖了大量关于处理溢出效应、社会互动、社会学习、信息扩散、疾病与金融传染、社会资本形成等主题的文献。我们的方法首先利用以路径距离衡量的邻近个体及其连接的配置来刻画个体在网络中的关联方式, 进而通过汇集配置相似的个体的结果数据来学习政策或处理分配的影响。我们通过推导平均处理响应的 k 近邻估计量均方误差的有限样本界, 并提出一个针对政策无关性假设的渐近有效检验, 来展示该方法。我们以模拟以及在社会资本形成方面的应用来说明该方法的实证适用性。
Abstract
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 deriving finite‐sample bounds on the mean‐squared error of a k‐nearest neighbor estimator for the average treatment response as well as proposing an asymptotically valid test for the hypothesis of policy irrelevance. We illustrate the empirical applicability of our method with simulations and an application to social capital formation.