Causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption may no longer hold. For instance, in the context of social research, the outcome of a study unit will likely be affected by an intervention or treatment received by close neighbors. While inverse probability-of-treatment weighted (IPW) estimators have been developed for this setting, they are often highly inefficient. In this work, we assume that the network is a union of disjoint components and propose doubly robust (DR) estimators combining models for treatment and outcome that are consistent and asymptotically normal if either model is correctly specified. We present empirical results that illustrate the DR property and the efficiency gain of DR over IPW estimators when both the outcome and treatment models are correctly specified. Simulations are conducted for networks with equal and unequal component sizes and outcome data with and without a multilevel structure. We apply these methods in an illustrative analysis using the Add Health network, examining the impact of maternal college education on adolescent school performance, both direct and indirect.

翻译：社会网络中的群体因果推断面临技术挑战，因为典型的无干扰假设可能不再成立。例如，在社会研究中，研究单位的结局很可能受到邻近个体接受的干预或处理影响。虽然针对该场景已开发出逆处理概率加权（IPW）估计量，但这些方法通常效率较低。本研究假设网络由不相交的组件构成，提出了一种结合处理模型和结局模型的双重稳健（DR）估计量，该估计量在任一模型正确设定时均能保持一致性和渐近正态性。我们通过实证结果展示了DR性质，并证明了当结局模型和处理模型均正确设定时，DR估计量相比IPW估计量的效率提升。针对网络组件规模相等与不等、结局数据含有多层结构与不含多层结构的情形进行了模拟实验。我们利用Add Health网络数据开展示例分析，检验母亲大学教育对青少年学业表现的影响，包含直接效应与间接效应。