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Uniform Inference on Quantile Effects under Network Interference
Preprint   Open access

Uniform Inference on Quantile Effects under Network Interference

Zequn Jin, Gaoqian Xu, Zixin Yang and Zhengyu Zhang
arXiv
arXiv
08/23/2026
DOI: 10.48550/arxiv.2608.22286
url
https://doi.org/10.48550/arxiv.2608.22286View
Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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 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 prina2015banking.

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