Causal Analysis at Extreme Quantiles with Application to London Traffic Flow Data

Transport engineers employ various interventions to enhance traffic-network performance. Recent emphasises on cycling as a sustainable travel mode aims to reduce traffic congestion. Quantifying the impacts of Cycle Superhighways is complicated due to the non-random assignment of such intervention over the transport network and heavy-tailed distribution of traffic flow. Treatment effects on asymmetric and heavy tailed distributions are better reflected at extreme tails rather than at averages or intermediate quantiles. In such situations, standard methods for estimating quantile treatment effects at the extremes can provide misleading inference due to the high variability of estimates. 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 bulk part of the distribution 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 analysis of London transport data utilising the proposed method indicates that the traffic flow increased substantially after the Cycle Superhighway came into operation. The findings can assist government agencies in effective decision making to avoid high consequence events and improve network performance.