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

Author

Listed:
  • Bhuyan, Prajamitra
  • Jana, Kaushik
  • McCoy, Emma J.

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.

Suggested Citation

  • Bhuyan, Prajamitra & Jana, Kaushik & McCoy, Emma J., 2023. "Causal analysis at extreme quantiles with application to London traffic flow data," LSE Research Online Documents on Economics 121622, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:121622
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    File URL: http://eprints.lse.ac.uk/121622/
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    References listed on IDEAS

    as
    1. Zou, Hui & Yuan, Ming, 2008. "Regularized simultaneous model selection in multiple quantiles regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5296-5304, August.
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    More about this item

    Keywords

    causality; extreme value analysis; heavy-tailed distribution; potential outcome; quantile regression; transport engineering; AAM requested;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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