Work

Causal analysis at extreme quantiles with application to London traffic flow data

Year: 2023

Type: article

Abstract: 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.

Source: Journal of the Royal Statistical Society Series C (Applied Statistics)

DOI: 10.1093/jrsssc/qlad080

Authors: Prajamitra Bhuyan, Kaushik Jana, Emma J McCoy

Institutions: Indian Institute of Management Calcutta, Ahmedabad University, London School of Economics and Political Science

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

Funder: Lloyd's Register