Estimating the average treatment effect in social networks is challenging due to individuals influencing each other. One approach to address interference is ego cluster experiments, where each cluster consists of a central individual (ego) and its peers (alters). Clusters are randomized, and only the effects on egos are measured. In this work, we propose an improved framework for ego cluster experiments called ego group partition (EGP), which directly generates two groups and an ego sub-population instead of ego clusters. Under specific model assumptions, we propose two ego group partition algorithms. Compared to the original ego clustering algorithm, our algorithms produce more egos, yield smaller biases, and support parallel computation. The performance of our algorithms is validated through simulation and real-world case studies.

翻译：在社交网络中，由于个体之间存在相互影响，估算平均处理效应具有挑战性。解决干扰问题的一种方法是采用个体中心聚类实验（ego cluster experiments），其中每个聚类由一个中心个体（ego）及其同伴（alters）组成。这些聚类被随机分配，仅测量对中心个体的影响。本研究提出了一种改进的个体中心聚类实验框架，称为群体划分（ego group partition，EGP），该框架直接生成两个群体和一个中心个体子群体，而非生成个体中心聚类。在特定模型假设下，我们提出了两种群体划分算法。与原始个体中心聚类算法相比，我们的算法能产出更多中心个体、产生更小的偏差，并支持并行计算。通过模拟实验和真实案例研究验证了算法的性能。