  If an experimental treatment is experienced by both treated and control group
units, tests of hypotheses about causal effects may be difficult to
conceptualize let alone execute. In this paper, we show how counterfactual
causal models may be written and tested when theories suggest spillover or
other network-based interference among experimental units. We show that the "no
interference" assumption need not constrain scholars who have interesting
questions about interference. We offer researchers the ability to model
theories about how treatment given to some units may come to influence outcomes
for other units. We further show how to test hypotheses about these causal
effects, and we provide tools to enable researchers to assess the operating
characteristics of their tests given their own models, designs, test
statistics, and data. The conceptual and methodological framework we develop
here is particularly applicable to social networks, but may be usefully
deployed whenever a researcher wonders about interference between units.
Interference between units need not be an untestable assumption; instead,
interference is an opportunity to ask meaningful questions about theoretically
interesting phenomena.
