Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of local interference, for instance, potential outcomes of a unit depend on its treatment as well as on the treatments of other local units, such as its neighbors according to the network. In observational studies, a further complication is that the typical unconfoundedness assumption must be extended - say, to include the treatment of neighbors, and indi- vidual and neighborhood covariates - to guarantee identification and valid inference. Here, we propose new estimands that define treatment and interference effects. We then derive analytical expressions for the bias of a naive estimator that wrongly assumes away interference. The bias depends on the level of interference but also on the degree of association between individual and neighborhood treatments. We propose an extended unconfoundedness assumption that accounts for interference, and we develop new covariate-adjustment methods that lead to valid estimates of treatment and interference effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. We carry out simulations, calibrated using friendship networks and covariates in a nationally representative longitudinal study of adolescents in grades 7-12, in the United States, to explore finite-sample performance in different realistic settings.

中文翻译：

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网络观测研究中处理和干扰效应的识别和估计

对通过网络连接的一组单元进行因果推断通常会带来技术挑战，包括如何解释干扰。例如，在存在本地干扰的情况下，一个单元的潜在结果取决于它的处理以及其他本地单元的处理，例如根据网络的邻居。在观察性研究中，更复杂的情况是必须扩展典型的无混杂假设——例如，包括对邻居的处理以及个人和邻居协变量——以保证识别和有效推理。在这里，我们提出了定义治疗和干扰效应的新估计量。然后，我们为错误地假设没有干扰的朴素估计器的偏差推导出解析表达式。偏差取决于干扰程度，但也取决于个人和邻里治疗之间的关联程度。我们提出了一个解释干扰的扩展无混杂假设，并且我们开发了新的协变量调整方法，从而在对网络的观察研究中对治疗和干扰影响进行有效估计。估计基于广义倾向评分，该评分在不同水平的个体治疗和接触邻居治疗的情况下平衡跨单位的个体和邻域协变量。我们在美国 7-12 年级青少年的全国代表性纵向研究中进行模拟，使用友谊网络和协变量进行校准，以探索不同现实环境中的有限样本表现。