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 \textit{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 \textit{interference units} on the other side of the graph. Examples of this type of interference are common in marketplaces and two-sided platforms. The \textit{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.

We would like to thank all three reviewers for their careful review and constructive feedback. " In the introduction the connection to optimal experimental design is alluded to. " Thank you for this feedback. "It is not clear what the quality of the approximation is for the heuristic presented [...]" We agree that the quality of the "What is the space complexity of the proposed heuristic?" "Why are other correlation clustering heuristics not compared against in the experiment section?" "There are no results given for the variance of the estimators / designs [...]" Thank you for this feedback.

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 \textit{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 \textit{interference units} on the other side of the graph. Examples of this type of interference are common in marketplaces and two-sided platforms. The \textit{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.