Corpus ID: 199577548

Causal discovery in heavy-tailed models

@article{Gnecco2019CausalDI,
  title={Causal discovery in heavy-tailed models},
  author={Nicola Gnecco and Nicolai Meinshausen and J. Peters and S. Engelke},
  journal={arXiv: Methodology},
  year={2019}
}
  • Nicola Gnecco, Nicolai Meinshausen, +1 author S. Engelke
  • Published 2019
  • Mathematics
  • arXiv: Methodology
  • Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms manifest themselves only in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population case, the causal… CONTINUE READING

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