Under interference, the potential outcomes of a unit depend on treatments assigned to other units. A network interference structure is typically assumed to be given and accurate. In this paper, we study the problems resulting from misspecifying these networks. First, we derive bounds on the bias arising from estimating causal effects under a misspecified network. We show that the maximal possible bias depends on the divergence between the assumed network and the true one with respect to the induced exposure probabilities. Then, we propose a novel estimator that leverages multiple networks simultaneously and is unbiased if one of the networks is correct, thus providing robustness to network specification. Additionally, we develop a probabilistic bias analysis that quantifies the impact of a postulated misspecification mechanism on the causal estimates. We illustrate key issues in simulations and demonstrate the utility of the proposed methods in a social network field experiment and a cluster-randomized trial with suspected cross-clusters contamination.

翻译：在干扰效应存在时，个体的潜在结果依赖于其他个体接受的处理分配。通常假设网络干扰结构是已知且准确的。本文研究因网络错误指定所引发的问题。首先，我们推导了在错误指定网络下估计因果效应所产生的偏差范围，表明最大可能偏差取决于假定网络与真实网络在诱导暴露概率上的差异。其次，我们提出一种能同时利用多个网络的新型估计量，若其中任意一个网络正确则估计无偏，从而对网络设定具有稳健性。此外，我们开发了概率偏差分析方法，用于量化假定的错误指定机制对因果估计的影响。通过仿真实验说明关键问题，并在一项社交网络田野实验及一项存在疑似跨集群污染的整群随机试验中展示了所提方法的应用价值。