AI 中文总结
本文针对网络干扰下的分位数效应推断问题，提出无需度分布与协方差结构稳定要求的一致置信带方法，经模拟与尼泊尔储蓄账户实验验证，拓展了网络实验分位数效应的推断框架。
AI 中文摘要
本文研究网络实验中的处理分位数效应与溢出分位数效应。平均溢出效应揭示了对某一单位的邻居进行处理如何平均影响其结果，但掩盖了这些效应在结果分布中的异质性。我们定义结构分位数效应，该效应比较不同暴露状态下的结果分位数，刻画自身处理及接触已处理邻居对结果分布不同部分的影响。基于Leung（2020）的研究，我们首先在要求度分布和依赖网络的协方差结构稳定的条件下，建立估计分位数效应过程的弱收敛。本文主要贡献是提出基于已实现网络条件下高斯近似的一致置信带（UCB），避免了上述稳定要求。通过大量模拟研究和对尼泊尔一项随机储蓄账户实验（Prina，2015）的实证应用，对所提方法进行评估。
英文摘要
This paper studies quantile treatment and spillover effects in network experiments. Average spillover effects reveal how treating a unit's neighbors affects its outcome on average, but mask the heterogeneity of these effects across the outcome distribution. We define structural quantile effects that compare outcome quantiles between exposure states, characterizing how own treatment and exposure to treated neighbors affect different parts of the outcome distribution. Building on \citet{leung2020treatment}, we first establish the weak convergence of the estimated quantile-effect process under conditions requiring the stabilization of the degree distribution and the network-dependent covariance structure. Our main contribution is to propose uniform confidence bands (UCBs) based on Gaussian approximations conditional on the realized network, avoiding these stabilization requirements. The proposed method is evaluated through extensive simulation studies and an empirical application to a randomized savings-account experiment in Nepal \citep{prina2015banking}.
Comments50 pages, 3 figures
