Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference

• 成果类型: Article
• 署名作者: Lee, Chanhwa; Zeng, Donglin; Hudgens, Michael G.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2340789
• 发表日期: 2025
• 页码: 382-394
• 关键词: Causal Inference

摘要

Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking real-world relevance or are based on parametric propensity score models. Here, we propose new causal estimands based on modification of the propensity score distribution which may be more relevant in many contexts and are not based on parametric models. Nonparametric sample splitting estimators of the new estimands are constructed, which allow for flexible data-adaptive estimation of nuisance functions and are consistent, asymptotically normal, and efficient, converging at the usual parametric rate. Simulations show the finite sample performance of the proposed estimators. The proposed methods are applied to evaluate the effect of water, sanitation, and hygiene facilities on diarrhea among children in Senegal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.