DESIGN-BASED INFERENCE FOR SPATIAL EXPERIMENTS UNDER UNKNOWN INTERFERENCE

• 成果类型: Article
• 署名作者: Wang, Ye; Samii, Cyrus; Chang, Haoge; Aronow, P. M.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; New York University; Columbia University; Yale University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1986
• 发表日期: 2025
• 页码: 744-768
• 关键词: Causal Inference; CONSERVATION

摘要

We consider design-based causal inference for spatial experiments in which treatments may have effects that bleed out and feed back in complex ways. Such spatial spillover effects violate the no interference assumption for standard causal inference methods. The complexity of spatial spillover effects also raises the risk of misspecification and bias in model-based analyses. We offer an approach for robust inference in such settings without having to specify a parametric outcome model. We define a spatial average marginalized effect (AME) that characterizes how, in expectation, units of observation that are a specified distance from an intervention location are affected by treatment at that location, averaging over effects emanating from other intervention nodes. We show that randomization is sufficient for nonparametric identification of the AME, even if the nature of interference is unknown. Under mild restrictions on the extent of interference, we establish asymptotic distributions of estimators and provide methods for both sample-theoretic and randomization-based inference. We show conditions under which the AME recovers a structural effect. We illustrate our approach with a simulation study. Then we reanalyze a randomized field experiment and a quasi-experiment on forest conservation, showing how our approach offers robust inference on policy-relevant spillover effects.