Estimation of interference effects in networks with community structures

Music City Center 

In causal inference, the interference effect – whether an individual's outcome is affected by the treatment of its neighbors – is gaining increasing attention. The majority of existing work assumes that the observed networks represent the true underlying interference networks. In practice, this assumption is not correct and leads to the bias in the estimation of causal effects. In this work, we address the problem of whether true interference effects exist given the observed networks. In particular, our proposed framework leverages the community structures in the networks and assumes the interference effects are identically distributed for individuals in the same community. We demonstrate that our proposed model is able to identify the interference effects in theory and in simulations. We apply our proposed framework to the stroke encounter data and evaluate the potential effect of performing EVT procedures in one hospital on its neighbors.

Interference Effect

Community Structure

Causal Inference 

Section on Statistical Graphics