This paper develops new methods for causal inference in observational studies on a single large network of interconnected units, addressing two key challenges: long-range dependence among units and the presence of general interference. We introduce a novel network version of Augmented Inverse Propensity Weighted, which combines propensity score and outcome models defined on the network to achieve doubly robust identification and estimation of both direct and spillover causal effects. Under a network version of conditional ignorability, the proposed approach identifies the expected potential outcome for a unit given the treatment assignment vector for its network neighborhood up to a user-specified distance, while marginalizing over treatment assignments for the rest of the network. Under the union of two Markov assumptions--one governing the propensity score model and the other the outcome model--we propose a corresponding semiparametric estimator based on general parametric specifications of nuisance functions. In particular, we suggest a class of parametric auto-regression models motivated by the Markov assumptions (Besag, 1974). By combining a restricted interference assumption with the propensity score model, we establish a new doubly robust identification result for the expected potential outcome under a hypothetical intervention on the treatment assignment vector for the entire network. We formally prove that, under weak network dependence, our proposed estimators are asymptotically normal and we characterize the impact of model misspecification on the asymptotic variance. Extensive simulation studies highlight the practical relevance of our approach. We further demonstrate its application in an empirical analysis of the NNAHRAY study, evaluating the impact of incarceration on individual socioeconomic outcomes in Brooklyn, New York.

翻译：本文针对观测性研究中单个大型互联单元网络中的因果推断，提出了新方法以应对两大关键挑战：单元间的长程依赖性与普遍干扰的存在。我们引入了一种新颖的网络版增强逆倾向加权方法，该方法结合了定义在网络上的倾向得分模型与结果模型，实现了对直接因果效应与溢出因果效应的双重稳健识别与估计。在网络条件可忽略性假设下，所提方法识别了给定单元网络邻域（至用户指定距离）内处理分配向量的期望潜在结果，同时对网络中其余部分的处理分配进行边际化处理。在两种马尔可夫假设（分别控制倾向得分模型与结果模型）的并集下，我们基于干扰函数的一般参数化设定，提出了一种相应的半参数估计量。特别地，我们建议了一类由马尔可夫假设（Besag, 1974）启发的参数自回归模型。通过将受限干扰假设与倾向得分模型相结合，我们为整个网络处理分配向量在假设干预下的期望潜在结果建立了一个新的双重稳健识别结果。我们严格证明了在弱网络依赖性条件下，所提估计量具有渐近正态性，并刻画了模型误设定对渐近方差的影响。大量模拟研究凸显了我们方法的实际相关性。我们进一步通过NNAHRAY研究的实证分析展示了其应用，评估了纽约布鲁克林地区监禁对个体社会经济结果的影响。