  We design randomization tests of heterogeneous treatment effects when units
interact on a single connected network. Our modeling strategy allows network
interference into the potential outcomes framework using the concept of
exposure mapping. We consider several null hypotheses representing different
notions of homogeneous treatment effects. However, these hypotheses are not
sharp due to nuisance parameters and multiple potential outcomes. To address
the issue of multiple potential outcomes, we propose a conditional
randomization method that expands on existing procedures. Our conditioning
approach permits the use of treatment assignment as a conditioning variable,
widening the range of application of the randomization method of inference. In
addition, we propose techniques that overcome the nuisance parameter issue. We
show that our resulting testing methods based on the conditioning procedure and
the strategies for handling nuisance parameters are asymptotically valid. We
demonstrate the testing methods using a network data set and also present the
findings of a Monte Carlo study.
