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

arXiv: https://arxiv.org/abs/2108.10215 · HTML: https://mlsanity.com/paper/2108.10215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test](https://mlsanity.com/paper/2211.16596/) (2022)
- [TSLiNGAM: DirectLiNGAM under heavy tails](https://mlsanity.com/paper/2308.05422/) (2023)

## Abstract (arXiv)

Transport engineers employ various interventions to enhance traffic-network performance. Quantifying the impacts of Cycle Superhighways is complicated due to the non-random assignment of such an intervention over the transport network. Treatment effects on asymmetric and heavy-tailed distributions are better reflected at extreme tails rather than at the median. We propose a novel method to estimate the treatment effect at extreme tails incorporating heavy-tailed features in the outcome distribution. The analysis of London transport data using the proposed method indicates that the extreme traffic flow increased substantially after Cycle Superhighways came into operation.