Testing for interactions between direct and indirect effects under experimental interference

Music City Center 

Treatment interference occurs when the treatment status of one unit affects the response of another unit. Substantial work has investigated various methods for modeling treatment interference and the impact of interference on units' responses.
The efficacy of these methods depends largely on the plausibility of the assumptions used for these methods. However, there has been little work at developing rigorous tests for these assumptions.
In this talk, we review various models of response for interference models under the Neyman-Rubin potential outcomes framework. We then outline certain assumptions that may be made on responses and the impact of these assumptions when estimating causal quantities. We then develop a framework for testing assumptions related to the weak interaction between direct and indirect effects. These assumptions require that the change of response due to a unit receiving treatment does not depend on which of that units' neighbors receive treatment. We evaluate the efficacy of these tests through a thorough simulation study.

Causal inference

potential outcomes

interference

heteroskedasticity

indirect effects

direct effects 

Section on Statistics in Epidemiology