Causal discovery in heavy-tailed models
By Nicola Gnecco, Nicolai Meinshausen, Jan Peters, Sebastian Engelke. Annals of Statistics, 2019
74 citations recorded by the source corpus.
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 only manifest themselves 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 tail coefficient is shown to reveal the causal structure if the distribution follows a linear structural causal model. This holds even in the presence of latent common causes that have the same tail index as the observed variables. Based on a consistent estimator of the causal tail coefficient, we propose a computationally highly efficient algorithm that estimates the causal structure. We prove that our method consistently recovers the causal order and we compare it to other well-established and non-extremal approaches in causal discovery on synthetic and real data. The code is available as an open-access R package.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/976fa0c70d0c517098385c56b36500e645da500a
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In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- Graphical models for extremes (2018, methodology): “malized maxima of independent copies of X and have been extensively studied and applied in multivariate and spatial risk problems (see de Haan (1984), Gudendorf and Segers (2010) and Davison et al.”