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

Prajamitra Bhuyan, Kaushik Jana and Emma J. McCoy

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: causality; extreme value analysis; heavy-tailed distribution; potential outcome; quantile regression; transport engineering; AAM requested (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2023-11-01
New Economics Papers: this item is included in nep-ecm, nep-tre and nep-ure
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Published in Journal of the Royal Statistical Society. Series C: Applied Statistics, 1, November, 2023, 72(5), pp. 1452 - 1474. ISSN: 0035-9254

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:121622

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