Causal inference under uniformly bounded neighbourhood interference

• 成果类型: Article; Early Access
• 署名作者: Lu, Xin; Li, Hongzi; Liu, Hanzhong
• 署名单位: Tsinghua University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkag077
• 发表日期: 2026-06-04
• 关键词: design-based inference; direct effect; Indirect Effect; interference; total average treatment effect

摘要

Randomized experiments remain the gold standard for estimating treatment effects; however, network interference compromises the validity of traditional estimators by violating the stable unit treatment value assumption and introducing bias. Although cluster-randomized designs help mitigate some bias, they struggle to accommodate complex network structures and cannot disentangle direct from indirect effects. To address these challenges, we develop a design-based asymptotic theory for Horvitz-Thompson estimators of the direct, indirect, and total average treatment effects under Bernoulli designs, assuming uniformly bounded neighbourhood interference. Given the poor performance of classical Horvitz-Thompson point and variance estimators in dense networks, we propose eigenvector-adjusted point estimators along with novel variance estimators to improve inference. We establish theoretical guarantees for the proposed estimators within the design-based framework, allowing for robust conclusions that are independent of the stochastic properties of the network or the potential outcome model. The adaptability of the method is showcased under two structured interference scenarios: partial interference and local interference in a two-sided marketplace. Numerical studies further highlight the practical utility of the proposed estimators.