In many observational studies in social science and medicine, subjects or units are connected, and one unit's treatment and attributes may affect another's treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, interference is heterogeneous and needs to be modeled explicitly. In this paper, we focus on the partial interference setting, and only restrict units to be exchangeable conditional on observable characteristics. Under this framework, we propose generalized augmented inverse propensity weighted (AIPW) estimators for general causal estimands that include heterogeneous direct and spillover effects. We show that they are semiparametric efficient and robust to heterogeneous interference as well as model misspecifications. We apply our methods to the Add Health dataset to study the direct effects of alcohol consumption on academic performance and the spillover effects of parental incarceration on adolescent well-being.

翻译：在社会科学和医学的许多观测研究中，个体或单元之间存在关联，一个单元的处理和属性可能影响另一个单元的处理与结果，这违背了稳定单元处理值假设（SUTVA）并导致干扰效应。为实现可行的估计与推断，已有研究多假设干扰单元（邻居）具有可交换性。然而，在具有显著异质性单元的应用场景中，干扰效应存在异质性，需要显式建模。本文聚焦于部分干扰场景，仅要求单元在可观测特征条件下具有可交换性。在此框架下，我们针对包含异质性直接效应与溢出效应的一般因果估计量，提出了广义增强逆倾向加权（AIPW）估计量。我们证明这些估计量具有半参数有效性，并对异质性干扰及模型误设具有稳健性。我们将所提方法应用于Add Health数据集，研究饮酒对学业表现的直接效应以及父母监禁对青少年心理健康的溢出效应。