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arXiv · 2307.10703

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

Víctor Elvira·Émilie Chouzenoux·Jordi Cerdà·Gustau Camps-Valls
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Abstract

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.

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Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà, Gustau Camps-Valls. 2023-07-20. Graphs in State-Space Models for Granger Causality in Climate Science. https://arxiv.org/abs/2307.10703

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