  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.
