Causal Inference under Network Interference with Noise

Understand

Increasingly, there is a marked interest in estimating causal effects under network interference due to the fact that interference manifests naturally in networked experiments.

• However, network information generally is available only up to some level of error.
• We study the propagation of such errors to estimators of average causal effects under network interference.
• Specifically, assuming a four-level exposure model and Bernoulli random assignment of treatment, we characterize the impact of network noise on the bias and variance of standard estimators in homogeneous and inhomogeneous networks.