MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913681309171712 |
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| author | Zhang, Elise Mirallès, François Rousseau-Rizzi, Raphaël Zinflou, Arnaud Wu, Di Boulet, Benoit |
| author_facet | Zhang, Elise Mirallès, François Rousseau-Rizzi, Raphaël Zinflou, Arnaud Wu, Di Boulet, Benoit |
| contents | Convergent Cross Mapping (CCM) is a powerful method for detecting causality in coupled nonlinear dynamical systems, providing a model-free approach to capture dynamic causal interactions. Partial Cross Mapping (PCM) was introduced as an extension of CCM to address indirect causality in three-variable systems by comparing cross-mapping quality between direct cause-effect mapping and indirect mapping through an intermediate conditioning variable. However, PCM remains limited to univariate delay embeddings in its cross-mapping processes. In this work, we extend PCM to the multivariate setting, introducing multiPCM, which leverages multivariate embeddings to more effectively distinguish indirect causal relationships. We further propose a multivariate cross-mapping framework (MXMap) for causal discovery in dynamical systems. This two-phase framework combines (1) pairwise CCM tests to establish an initial causal graph and (2) multiPCM to refine the graph by pruning indirect causal connections. Through experiments on simulated data and the ERA5 Reanalysis weather dataset, we demonstrate the effectiveness of MXMap. Additionally, MXMap is compared against several baseline methods, showing advantages in accuracy and causal graph refinement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems Zhang, Elise Mirallès, François Rousseau-Rizzi, Raphaël Zinflou, Arnaud Wu, Di Boulet, Benoit Machine Learning Dynamical Systems Methodology Convergent Cross Mapping (CCM) is a powerful method for detecting causality in coupled nonlinear dynamical systems, providing a model-free approach to capture dynamic causal interactions. Partial Cross Mapping (PCM) was introduced as an extension of CCM to address indirect causality in three-variable systems by comparing cross-mapping quality between direct cause-effect mapping and indirect mapping through an intermediate conditioning variable. However, PCM remains limited to univariate delay embeddings in its cross-mapping processes. In this work, we extend PCM to the multivariate setting, introducing multiPCM, which leverages multivariate embeddings to more effectively distinguish indirect causal relationships. We further propose a multivariate cross-mapping framework (MXMap) for causal discovery in dynamical systems. This two-phase framework combines (1) pairwise CCM tests to establish an initial causal graph and (2) multiPCM to refine the graph by pruning indirect causal connections. Through experiments on simulated data and the ERA5 Reanalysis weather dataset, we demonstrate the effectiveness of MXMap. Additionally, MXMap is compared against several baseline methods, showing advantages in accuracy and causal graph refinement. |
| title | MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems |
| topic | Machine Learning Dynamical Systems Methodology |
| url | https://arxiv.org/abs/2502.03802 |