Causal discovery in heavy‐tailed linear structural equation models via scalings
针对重尾噪声的线性结构方程模型，提出一种基于极值角度测度缩放参数的因果发现方法，通过模拟和河流流量数据验证其一致性，并引入稳定性超参数选择，性能优于现有极值方法。
Abstract Causal dependence modelling of multivariate extremes is intended to improve our understanding of the relationships among variables associated with rare events. Regular variation provides a standard framework in the study of extremes. This paper concerns the extremal causal dependence of the linear structural equation model with regularly varying noise variables. We focus on extreme observations generated from such a model and propose a causal discovery method based on the scaling parameters of its extremal angular measure. We implement the method as an algorithm, establish its consistency and evaluate it by simulation and by application to river discharge datasets. We propose a selection procedure for its hyperparameters based on a notion of stability. Comparison with the only alternative extremal method for such model reveals its competitive performance.