  The conclusions of randomized controlled trials may be biased when the
outcome of one unit depends on the treatment status of other units, a problem
known as interference. In this work, we study interference in the setting of
one-sided bipartite experiments in which the experimental units - where
treatments are randomized and outcomes are measured - do not interact directly.
Instead, their interactions are mediated through their connections to
interference units on the other side of the graph. Examples of this type of
interference are common in marketplaces and two-sided platforms. The
cluster-randomized design is a popular method to mitigate interference when the
graph is known, but it has not been well-studied in the one-sided bipartite
experiment setting. In this work, we formalize a natural model for interference
in one-sided bipartite experiments using the exposure mapping framework. We
first exhibit settings under which existing cluster-randomized designs fail to
properly mitigate interference under this model. We then show that minimizing
the bias of the difference-in-means estimator under our model results in a
balanced partitioning clustering objective with a natural interpretation. We
further prove that our design is minimax optimal over the class of linear
potential outcomes models with bounded interference. We conclude by providing
theoretical and experimental evidence of the robustness of our design to a
variety of interference graphs and potential outcomes models.
