Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interference occurs only among individuals within the same cluster, a setting referred to as partial interference. We study network causal effects on outcome quantiles in the presence of partial interference. We develop a general nonparametric efficiency theory for estimating these network quantile causal effects, which leads to a nonparametrically efficient estimator. The proposed estimator is consistent and asymptotically normal with parametric convergence rates, while allowing for flexible, data-adaptive estimation of complex nuisance functions. We leverage a three-way cross-fitting procedure that avoids direct estimation of the conditional outcome distribution. Simulations demonstrate adequate finite-sample performance of the proposed estimators, and we apply the methods to a clustered observational study.

翻译：干扰是指对某个个体的处理会影响其他个体的结果。通常，个体自然地分组为若干集群，且干扰仅发生在同一集群内的个体之间，这种设置称为部分干扰。我们研究在部分干扰存在时网络因果效应对结果分位数的影响。我们发展了一套用于估计这些网络分位数因果效应的非参数效率理论，从而得到非参数高效的估计量。所提出的估计量具有相合性和渐近正态性，收敛速度达到参数速率，同时允许对复杂讨厌函数进行灵活的数据自适应估计。我们利用三重交叉拟合过程，避免直接估计条件结果分布。模拟实验表明所提估计量在有限样本下表现良好，并将该方法应用于一项集群观测研究。