Publication

Causal discovery in heavy‐tailed linear structural equation models via scalings

2025

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.

Official source

https://infoscience.epfl.ch/entities/publication/3f54ac75-f17e-46c5-9235-83ed7a1bc998

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3f54ac75-f17e-46c5-9235-83ed7a1bc998

About this result

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Causality

Causality (also called causation, or cause and effect) is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.

Causal model

In the philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial.