This study considers testing the specification of spillover effects in causal inference. We focus on experimental settings in which the treatment assignment mechanism is known to researchers. We develop a new randomization test utilizing a hierarchical relationship between different exposures. Compared with existing approaches, our approach is essentially applicable to any null exposure specifications and produces powerful test statistics without a priori knowledge of the true interference structure. As empirical illustrations, we revisit two existing social network experiments: one on farmers' insurance adoption and the other on anti-conflict education programs.

翻译：本研究探讨因果推断中溢出效应设定的检验问题。我们重点关注研究者已知处理分配机制的实验场景。基于不同处理暴露之间的层级关系，本文提出一种新型随机化检验方法。与现有方法相比，本方法本质上适用于任意零假设下的暴露设定，且无需事先了解真实干扰结构即可生成具有统计检验力的检验统计量。作为实证例证，我们重新考察了两项现有社会网络实验：一项关于农户保险采纳行为，另一项涉及反冲突教育项目。