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Increasingly, there is a marked interest in estimating causal effects undernetwork interference due to the fact that interference manifests naturally innetworked experiments. However, network information generally is available onlyup to some level of error. We study the propagation of such errors toestimators of average causal effects under network interference. Specifically,assuming a four-level exposure model and Bernoulli random assignment oftreatment, we characterize the impact of network noise on the bias and varianceof standard estimators in homogeneous and inhomogeneous networks. In addition,we propose method-of-moments estimators for bias reduction where a minimalnumber of network replicates are available. We show our estimators areasymptotically normal and provide confidence intervals for quantifying theuncertainty in these estimates. We illustrate the practical performance of ourestimators through simulation studies in British secondary school contactnetworks.

Causal Inference under Network Interference with Noise