Robust Signal Maximization in Spillover Experiments

We study the optimal design and analysis of experiments for estimating spillover effects. Assuming a known (e.g., linear) exposure mapping, we characterize the treatment-assignment distribution and regression-based estimator that minimize worst-case asymptotic variance against a broad class of distributions of unobservables. The design problem yields an intuitive solution in which the planner trades off spillover signal strength against diffusion of spillover variation. The analysis problem yields a simple recentered instrumental variable estimator to best leverage this variation. This framework produces natural solutions in several benchmark cases - such as clustered exposure - and suggests computationally tractable approximations for general networks, including bipartite settings. We illustrate these new tools in semi-synthetic experiments based on two applications from development economics. Our approach yields large standard error reductions in both experiments, increasing effective sample sizes by 50-100% or more.