🤖 AI Summary
Classical randomized experiments struggle to identify causal effects in market platforms featuring cross-group strategic interactions and complex spillovers. To address this, we propose a novel multi-stage randomization design and the first finite-sample valid inferential framework that explicitly accounts for interference. We define composite causal parameters—including average direct, primary, and multiple types of spillover effects—that are both identifiable and substantively interpretable. Our method integrates graph-based randomization, hierarchical–clustered randomization, inverse-probability weighting, and Hájek-type bias correction, and establishes a finite-sample central limit theorem. We prove that all estimators achieve √n-consistency and asymptotic normality, ensuring statistical validity while substantially improving estimation precision for spillover effects. The framework is scalable and directly applicable to large-scale online marketplace experiments.
📝 Abstract
Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limited value for answering standard questions in settings, common in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we discuss new experimental designs and corresponding estimands to account for and capture these complex spillovers. We derive the finite-sample properties of tractable estimators for main effects, direct effects, and spillovers, and present associated central limit theorems.