Problem
Research questions and friction points this paper is trying to address.
Quantile Effects
Network Interference
Spillover Effects
Uniform Inference on Quantile Effects under Network Interference
🤖 AI Summary
本文研究了网络实验中的分位数处理和溢出效应，提出了一种基于高斯逼近的统一置信带方法来估计这些效应，避免了对度分布稳定性的要求。
📝 Abstract
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}.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uniform Confidence Bands
Gaussian Approximations
Network Interference
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