We propose an easy-to-use adjustment estimator for the effect of a treatment based on observational data from a single (social) network of units. The approach allows for interactions among units within the network, called interference, and for observed confounding. We define a simplified causal graph that does not differentiate between units, called generic graph. Using valid adjustment sets determined in the generic graph, we can identify the treatment effect and build a corresponding estimator. We establish the estimator's consistency and its convergence to a Gaussian limiting distribution at the parametric rate under certain regularity conditions that restrict the growth of dependencies among units. We empirically verify the theoretical properties of our estimator through a simulation study and apply it to estimate the effect of a strict facial-mask policy on the spread of COVID-19 in Switzerland.

翻译：我们提出了一种简便的调整估计量，用于基于单个（社会）网络单元观测数据来估计处理效应。该方法允许网络内单元之间存在交互（称为干扰），并考虑了可观测的混杂因素。我们定义了一种不区分单元的简化因果图，称为泛化图。通过利用在泛化图中确定的有效调整集，我们可以识别处理效应并构建相应的估计量。在限制单元间依赖增长的正则条件下，我们建立了该估计量的一致性，以及其以参数速率收敛到高斯极限分布的性质。我们通过模拟研究实证验证了估计量的理论特性，并将其应用于评估瑞士严格口罩政策对COVID-19传播的影响。