Interference is ubiquitous when conducting causal experiments over networks. Except for certain network structures, causal inference on the network in the presence of interference is difficult due to the entanglement between the treatment assignments and the interference levels. In this article, we conduct causal inference under interference on an observed, sparse but connected network, and we propose a novel design of experiments based on an independent set. Compared to conventional designs, the independent-set design focuses on an independent subset of data and controls their interference exposures through the assignments to the rest (auxiliary set). We provide a lower bound on the size of the independent set from a greedy algorithm , and justify the theoretical performance of estimators under the proposed design. Our approach is capable of estimating both spillover effects and treatment effects. We justify its superiority over conventional methods and illustrate the empirical performance through simulations.

翻译：在网络环境下进行因果实验时，干扰现象普遍存在。除特定网络结构外，由于处理分配与干扰水平之间的相互纠缠，存在干扰的网络因果推断十分困难。本文针对一个观测到的稀疏但连通的网络，在干扰条件下开展因果推断，并提出一种基于独立集的新型实验设计。与传统设计相比，独立集设计聚焦于数据的独立子集，并通过向其余子集（辅助集）分配处理来控制其干扰暴露程度。我们通过贪心算法给出了独立集大小的下界，并验证了该设计下估计量的理论性能。该方法能够同时估计溢出效应和处理效应，我们论证了其相对于传统方法的优越性，并通过模拟实验展示了其实证表现。