  A/B tests are often required to be conducted on subjects that might have
social connections. For e.g., experiments on social media, or medical and
social interventions to control the spread of an epidemic. In such settings,
the SUTVA assumption for randomized-controlled trials is violated due to
network interference, or spill-over effects, as treatments to group A can
potentially also affect the control group B. When the underlying social network
is known exactly, prior works have demonstrated how to conduct A/B tests
adequately to estimate the global average treatment effect (GATE). However, in
practice, it is often impossible to obtain knowledge about the exact underlying
network. In this paper, we present UNITE: a novel estimator that relax this
assumption and can identify GATE while only relying on knowledge of the
superset of neighbors for any subject in the graph. Through theoretical
analysis and extensive experiments, we show that the proposed approach performs
better in comparison to standard estimators.
