Graphs in State-Space Models for Granger Causality in Climate Science - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Graphs in State-Space Models for Granger Causality in Climate Science

Résumé

Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disciplines, from neuroscience and econometrics to Earth sciences. We revisit GC under a graphical perspective of state-space models. For that, we use GraphEM, a recently presented expectation-maximisation algorithm for estimating the linear matrix operator in the state equation of a linear-Gaussian state-space model. Lasso regularisation is included in the M-step, which is solved using a proximal splitting Douglas-Rachford algorithm. Experiments in toy examples and challenging climate problems illustrate the benefits of the proposed model and inference technique over standard Granger causality methods.
Fichier principal
Vignette du fichier
GraphEM_for_EO.pdf (589.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04165347 , version 1 (18-07-2023)

Licence

Paternité

Identifiants

  • HAL Id : hal-04165347 , version 1

Citer

Víctor Elvira, Emilie Chouzenoux, Jordi Cerda, Gustau Camps-Valls. Graphs in State-Space Models for Granger Causality in Climate Science. Colloquium 2023 - When Causal Inference Meets Statistical Analysis, Apr 2023, Paris, France. ⟨hal-04165347⟩
23 Consultations
23 Téléchargements

Partager

Gmail Facebook X LinkedIn More