Abstract:
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Learning causal structures from observational data is challenging. However, in applications such as hydrology, meteorology, or finance, one can exploit the heavy-tailed distribution of the data to recover causal information. This talk discusses a recent methodology that identifies and learns causal information from a linear structural causal model (SCM) with heavy-tailed noise (Gnecco et al., 2021). The first part will introduce the causal tail coefficient and the related EASE algorithm, which recovers a causal order of the underlying SCM. The second part will discuss recent results on the asymptotic distribution of the causal tail coefficient estimator for a broad spectrum of tail dependence settings.
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