  Cluster-randomized trials often involve units that are irregularly
distributed in space without well-separated communities. In these settings,
cluster construction is a critical aspect of the design due to the potential
for cross-cluster interference. The existing literature relies on partial
interference models, which take clusters as given and assume no cross-cluster
interference. We relax this assumption by allowing interference to decay with
geographic distance between units. This induces a bias-variance trade-off:
constructing fewer, larger clusters reduces bias due to interference but
increases variance. We propose new estimators that exclude units most
potentially impacted by cross-cluster interference and show that this
substantially reduces asymptotic bias relative to conventional
difference-in-means estimators. We provide formal justification for a new
design that chooses the number of clusters to balance the asymptotic bias and
variance of our estimators and uses unsupervised learning to automate cluster
construction.
