Assimilative Causal Inference
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917283439312896 |
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| author | Andreou, Marios Chen, Nan Bollt, Erik |
| author_facet | Andreou, Marios Chen, Nan Bollt, Erik |
| contents | Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. In this paper, assimilative causal inference (ACI) is developed, which is a methodological framework that leverages Bayesian data assimilation to trace causes backward from observed effects. ACI solves the inverse problem rather than quantifying forward influence. It uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and, in principle, can be implemented in high-dimensional settings by employing efficient data assimilation algorithms. Crucially, it provides online tracking of causal roles that may reverse intermittently and facilitates a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events. ACI opens valuable pathways for studying complex systems, where transient causal structures are critical. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_14825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assimilative Causal Inference Andreou, Marios Chen, Nan Bollt, Erik Machine Learning Statistics Theory Data Analysis, Statistics and Probability Methodology 62F15, 62D20, 62M20, 93E11, 93E14, 60H10 Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. In this paper, assimilative causal inference (ACI) is developed, which is a methodological framework that leverages Bayesian data assimilation to trace causes backward from observed effects. ACI solves the inverse problem rather than quantifying forward influence. It uniquely identifies dynamic causal interactions without requiring observations of candidate causes, accommodates short datasets, and, in principle, can be implemented in high-dimensional settings by employing efficient data assimilation algorithms. Crucially, it provides online tracking of causal roles that may reverse intermittently and facilitates a mathematically rigorous criterion for the causal influence range, revealing how far effects propagate. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events. ACI opens valuable pathways for studying complex systems, where transient causal structures are critical. |
| title | Assimilative Causal Inference |
| topic | Machine Learning Statistics Theory Data Analysis, Statistics and Probability Methodology 62F15, 62D20, 62M20, 93E11, 93E14, 60H10 |
| url | https://arxiv.org/abs/2505.14825 |