Robust Signal Maximization in Spillover Experiments

2026-07-20

AI Summary

This study addresses the challenge of unobserved confounding in experiments with spillover effects by optimizing both treatment assignment and estimation to minimize worst-case asymptotic variance. It characterizes the optimal treatment assignment distribution and integrates it with exposure mapping to maximize spillover signal strength while controlling its variability. The authors further develop a recentered instrumental variable estimator that efficiently leverages spillover-induced variation. Building on this, they derive experimental design principles that jointly account for signal and noise, along with computationally tractable approximation schemes applicable to clustered exposures and general networks—including bipartite graphs. In a semi-synthetic experiment drawn from development economics, the proposed approach substantially reduces standard errors, yielding effective sample size gains of 50% to over 100%.

Abstract

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.

Problem

• spillover effects
• experimental design
• asymptotic variance
• exposure mapping
• treatment assignment

Innovation

• spillover effects
• experimental design
• robust estimation
• instrumental variables
• network interference