Independent-Set Design of Experiments for Estimating Treatment and Spillover Effects under Network Interference
网络干扰下估计处理效应与溢出效应的独立集实验设计
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…