Network interference occurs when a unit's outcome depends not only on its own treatment but also on the treatments received by connected units in the network. Experimental designs and analysis methods that ignore such interference can yield biased estimators of causal effects. In this paper, we develop a new experimental design for the estimation and inference of global treatment effect and spillover effect under a model-based framework and ego-cluster randomization. Under this design, the network is partitioned into a collection of ego-clusters, each consisting of a focal unit (the ego) and its network neighbors (the alters), with randomization conducted at the cluster level. We propose model-based estimators for the global treatment effect and spillover effect and establish their consistency and asymptotic normality, with asymptotic variances determined by the ego-cluster structure. Building on these theoretical results, we introduce an ego-clustering algorithm that sequentially selects egos and assigns alters to minimize asymptotic variances. Simulation studies and two empirical applications demonstrate that the proposed procedure yields accurate inference and efficiency improvements over existing network experimental designs.

翻译：网络干扰现象是指一个单元的结果不仅取决于其自身的处理，还受到网络中相连单元处理的影响。忽视此类干扰的实验设计与分析方法可能导致因果效应估计产生偏差。本文在基于模型的框架与自我-集群随机化条件下，提出了一种用于全局处理效应和溢出效应估计与推断的新型实验设计。在该设计下，网络被划分为若干自我-集群，每个集群由一个焦点单元（自我）及其网络邻居（他者）构成，随机化在集群层面进行。我们提出了全局处理效应与溢出效应的基于模型的估计量，并证明了其一致性与渐近正态性，其渐近方差由自我-集群结构决定。基于这些理论结果，我们引入了一种自我-集群算法，通过序贯选择自我并分配他者来最小化渐近方差。仿真研究与两项实证应用表明，所提方法能够提供准确的推断，并在效率上优于现有网络实验设计。