Treatment effects on asymmetric and heavy tailed distributions are better reflected at extreme tails rather than at averages or intermediate quantiles. In such distributions, standard methods for estimating quantile treatment effects can provide misleading inference due to the high variability of the estimators at the extremes. In this work, we propose a novel method which incorporates a heavy tailed component in the outcome distribution to estimate the extreme tails and simultaneously employs quantile regression to model the remainder of the distribution. The threshold between the bulk of the distribution and the extreme tails is estimated by utilising a state of the art technique. Simulation results show the superiority of the proposed method over existing estimators for quantile causal effects at extremes in the case of heavy tailed distributions. The method is applied to analyse a real dataset on the London transport network. In this application, the methodology proposed can assist in effective decision making to improve network performance, where causal inference in the extremes for heavy tailed distributions is often a key aim.
翻译:对非对称和重型尾巴分布的处理效果在极端尾巴而不是平均或中间四分位分布中反映得更好。在这种分布中,估计四分位处理效果的标准方法可能会由于极端偏差的估测者变化很大而产生误导性推论。在这项工作中,我们提出了一个新颖的方法,在结果分布中包含一个重尾巴成分,以估计极端尾巴,同时使用四分位回归来模拟分布的剩余部分。大部分分布和极端尾巴之间的阈值是通过利用一种先进技术来估计的。模拟结果显示,在严重尾巴分布的情况下,拟议方法优于现有估计极端的定量因果关系效应的估测器。该方法用于分析伦敦运输网络的真实数据集。在这一应用中,拟议方法有助于有效决策,以改进网络的性能,在这种情况下,重尾巴分布极端的因果关系通常是一个关键目标。