CAUSAL DISCOVERY IN HEAVY-TAILED MODELS

• 成果类型: Article
• 署名作者: Gnecco, Nicola; Meinshausen, Nicolai; Peters, Jonas; Engelke, Sebastian
• 署名单位: University of Geneva; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/20-AOS2021
• 发表日期: 2021
• 页码: 1755-1778
• 关键词: inference

摘要

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 nonextremal approaches in causal discovery on synthetic and real data. The code is available as an open-access R package.