Graphs in State-Space Models for Granger Causality in Climate Science
| Autor: | Elvira, Víctor, Chouzenoux, Émilie, Cerdà, Jordi, Camps-Valls, Gustau |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 4 pages, 2 figures, 3 tables, CausalStats23: When Causal Inference meets Statistical Analysis, April 17-21, 2023, Paris, France |
| Databáze: | arXiv |
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