Identifying Treatment and Spillover Effects Using Exposure Contrasts

Abstract

To report spillover effects, a common approach is to regress outcomes on statistics summarizing neighbors' treatments. This paper studies nonparametric analogs of these estimands, which we refer to as exposure contrasts. We demonstrate that they may have the opposite sign of the unit-level effects of interest even under unconfoundedness. We then provide interpretable conditions on interference and the assignment mechanism under which exposure contrasts can be represented as convex averages of the unit-level effects, thereby avoiding sign reversals. These conditions encompass cluster-randomized trials, network experiments, and observational settings with peer effects in selection into treatment.