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:社会网络中嵌入人口的因果推断带来了技术挑战,因为典型的不干预假设可能不再能维持,例如,在社会研究方面,研究单位的结果可能受到近邻的干预或治疗的影响。虽然为这一环境开发了反向治疗权加权估测器(IPW),但它们往往效率极低。在这项工作中,我们假设网络是一个分解组件的组合,并提出了双重强健的(DR)估计器,将治疗模式和结果合并起来,如果任一模型都得到正确的说明,这些模型都是一致的和不中性正常的。我们介绍了实验结果,在结果和治疗模型都得到正确说明时,对IPW估计器的DR属性和效率收益进行了说明。对具有等和不平等成分大小的网络进行了模拟,并且没有多层次结构的结果数据。我们使用添加式健康网络在说明性分析中运用了这些方法,同时审查了妇校教育对青少年学校表现的直接和间接影响。