A0805
Title: Design-based weighted regression estimators for average and conditional spillover effects
Authors: Laura Forastiere - Yale University (United States) [presenting]
Fei Fang - Yale University (United States)
Abstract: When individuals engage in social or physical interactions, a units outcome may depend on the treatments received by others. In such interference environments, we provide a unified framework characterizing a broad class of spillover estimands as weighted averages of unit-to-unit spillover effects, with estimand-specific weights. We then develop design-based weighted least squares (WLS) estimators for both average and conditional spillover effects. We introduce three nonparametric estimators under the dyadic, sender, and receiver perspectives, which distribute the estimand weights differently across the outcome vector, design matrix, and weight matrix. For the average-type estimands, we show that all three estimators are equivalent to the Hajek estimator. For conditional spillover effects, we establish conditions under which the estimands are consistent for the target conditionalspillover effects. We further derive concentration inequalities, a central limit theorem, and conservative variance estimators in an asymptotic regime where both the number of clusters and cluster sizes grow. Simulation studies assess the finite-sample performance of our estimators. The utility of our methods is then illustrated through the analysis of a randomized experiment assessing the spillover effects of an information session on the uptake of a weather insurance among rice farmers in China.
