Design-based Estimation and Inference on Quantile Exposure Effect under General Interference
一般干扰下分位数暴露效应的设计型估计与推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究网络干扰下的因果暴露效应、分位数效应及直接与溢出效应。
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一般干扰下分位数暴露效应的设计型估计与推断
- 研究问题
- 网络干扰使同一暴露对应多种邻域处理配置时，如何定义可解释的分位数暴露效应，并进行有限总体设计型推断？
- 方法与思路
- 作者通过条件邻域依赖研究分位数估计，在明确网络度数条件下建立渐近正态性与核密度局部一致收敛，并构造保守区间。
- 材料陈述的贡献
- 摘要称，该框架涵盖分位数直接与溢出效应，刻画异质得分均值导致的方差估计偏差，并揭示平均效应分析遗漏的尾部影响。
- 阅读关注点
- 应核对暴露配置的聚合权重、随机化设计与网络度数条件，以及非光滑分位数的处理、方差偏差和区间保守性的来源。
- 阅读边界
- 本资料为 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出具体网络条件、暴露概率限制或教育应用的数值结果。
原文摘要
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.