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Cluster-randomized trials often involve units that are irregularlydistributed in space without well-separated communities. In these settings,cluster construction is a critical aspect of the design due to the potentialfor cross-cluster interference. The existing literature relies on partialinterference models, which take clusters as given and assume no cross-clusterinterference. We relax this assumption by allowing interference to decay withgeographic distance between units. This induces a bias-variance trade-off:constructing fewer, larger clusters reduces bias due to interference butincreases variance. We propose new estimators that exclude units mostpotentially impacted by cross-cluster interference and show that thissubstantially reduces asymptotic bias relative to conventionaldifference-in-means estimators. We provide formal justification for a newdesign that chooses the number of clusters to balance the asymptotic bias andvariance of our estimators and uses unsupervised learning to automate clusterconstruction.

Design of Cluster-Randomized Trials with Cross-Cluster Interference