基于设计的泛干扰下分位数暴露效应的估计与推断
Design-based Estimation and Inference on Quantile Exposure Effect under General Interference
- Peking University（北京大学）
- University of Miami（迈阿密大学）
- Beijing International Center for Mathematical Research（北京国际数学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对网络干扰下分位数暴露效应，提出基于设计的估计与推断框架，解决非光滑性与有限总体挑战，并通过模拟和教育干预应用揭示平均效应遗漏的尾部异质影响。
AI中文摘要：
公共卫生、环境科学和经济学中的许多应用都涉及相连单元之间的溢出效应，这违反了经典分位数处理效应方法所依赖的稳定单元处理值假设（SUTVA）。我们开发了一个通用框架，用于在网络干扰下定义、估计和推断分位数暴露效应（QEEs），其中分位数直接效应和溢出效应作为主要案例。在干扰下研究QEEs面临三个实质性挑战。首先，由于单一暴露水平代表许多邻域处理配置，因果估计量必须以可解释的方式聚合这些配置。其次，分位数估计量本质上是不光滑的，这使得它们超出了许多仅涉及Lipschitz或可微泛函的现有网络理论。第三，在基于设计的有限总体网络设置中，无法直接施加关于结果密度的常规光滑性假设。利用条件邻域依赖性，我们在明确的网络度条件下建立了渐近正态性，并推导了核密度估计的局部一致收敛速率。我们还刻画了由异质单元特定得分均值引起的方差估计偏差，并构建了渐近保守的置信区间。大量模拟和对学校友谊网络中一项教育干预的应用表明，所提出的框架揭示了异质暴露效应，包括尾部特定影响，而这些是仅基于平均处理效应分析所遗漏的。
英文摘要：
Many applications in public health, environmental science, and economics feature spillovers across connected units, violating the Stable Unit Treatment Value Assumption (SUTVA) underlying classical quantile treatment effect methods. We develop a general framework for defining, estimating, and conducting inference for quantile exposure effects (QEEs) under network interference, encompassing quantile direct and spillover effects as leading cases. Studying QEEs under interference faces three substantive challenges. First, because a single exposure level represents many neighborhood treatment configurations, the causal estimand must aggregate over these configurations in an interpretable manner. Second, quantile estimands are intrinsically non-smooth, placing them outside much of the existing network theory involving only Lipschitz or differentiable functionals. Third, in design-based, finite-population network settings, conventional smoothness assumptions on outcome densities cannot be imposed directly. Using conditional neighborhood dependence, we establish asymptotic normality under explicit network degree conditions and derive a locally uniform convergence rate for kernel density estimation. We also characterize the variance estimation bias arising from heterogeneous unit-specific score means and construct asymptotically conservative confidence intervals. Extensive simulations and an application to an educational intervention in school friendship networks demonstrate that the proposed framework reveals heterogeneous exposure effects, including tail-specific impacts, that are missed by analyses based solely on average treatment effects.
