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：社会网络嵌入群体中的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，在社会研究背景下，研究单位的结果很可能受到邻近邻居所接受的干预或处理的影响。虽然针对这一场景已发展出逆处理概率加权（IPW）估计量，但其通常效率极低。在本工作中，我们假设网络由不相交的组件并集构成，并提出结合处理模型与结果模型的双重稳健（DR）估计量，该估计量在任一模型正确设定时均具有一致性和渐近正态性。我们通过实证结果展示了DR性质，以及当结果模型和处理模型均正确设定时DR相较于IPW估计量的效率提升。针对网络组件规模相等与不等、结果数据包含或不包含多层次结构等情形开展了仿真实验。我们运用这些方法对Add Health网络进行示例性分析，考察母亲大学教育对青少年学业表现的直接与间接影响。