Detecting treatment interference under K-nearest-neighbors interference

Abstract

We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choose focal units under this model of interference. We then conduct a simulation study to evaluate the efficacy of existing methods for detecting network interference. We show that this choice of focal units leads to powerful tests of treatment interference that outperform current experimental methods.

Keywords

• causal inference
• networks
• peer effects
• randomized experiment
• spillover
• 62g10