A Method for Detecting & Characterizing Interference in Field Experiments

• In Event: Causal Inference: Design and Generalization

Abstract

With the rise of online social networks, there has been growing interest in modeling how experimental units influence each another---a phenomenon known as ``interference'' in the causal inference literature. Quantitative models of interference generally require knowledge of the way in which units are connected. And yet, in most field experiments, such data is unavailable. In this paper, we propose a general method for detecting and characterizing interference when the relationship data is unobserved. We provide analytical guarantees of the method's performance and we illustrate its behavior on simulated data. Finally, we apply the method to over fifteen political science field experiments and characterize the results via regression analysis.

Author

• Connor T Jerzak, University of Texas at Austin