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 an intervention over the transport network and heavy-tailed distribution of traffic flow. Treatment effects on asymmetric and the 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 to estimate the treatment effect at extreme tails incorporating heavy-tailed feature in the outcome distribution. Simulation results show the superiority of the proposed method over existing estimators for quantile causal effects at extremes. 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.