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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.19469 |
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| _version_ | 1866914988908609536 |
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| author | Telcs, András Kurbucz, Marcell T. Jakovác, Antal |
| author_facet | Telcs, András Kurbucz, Marcell T. Jakovác, Antal |
| contents | Temporally evolving systems are typically modeled by dynamic equations. A key challenge in accurate modeling is understanding the causal relationships between subsystems, as well as identifying the presence and influence of unobserved hidden drivers on the observed dynamics. This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems, whether deterministic or stochastic. Notably, the method also uncovers hidden common causes beyond the observed variables. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unified Causality Analysis Based on the Degrees of Freedom Telcs, András Kurbucz, Marcell T. Jakovác, Antal Methodology Artificial Intelligence Machine Learning Econometrics Mathematical Physics Temporally evolving systems are typically modeled by dynamic equations. A key challenge in accurate modeling is understanding the causal relationships between subsystems, as well as identifying the presence and influence of unobserved hidden drivers on the observed dynamics. This paper presents a unified method capable of identifying fundamental causal relationships between pairs of systems, whether deterministic or stochastic. Notably, the method also uncovers hidden common causes beyond the observed variables. By analyzing the degrees of freedom in the system, our approach provides a more comprehensive understanding of both causal influence and hidden confounders. This unified framework is validated through theoretical models and simulations, demonstrating its robustness and potential for broader application. |
| title | Unified Causality Analysis Based on the Degrees of Freedom |
| topic | Methodology Artificial Intelligence Machine Learning Econometrics Mathematical Physics |
| url | https://arxiv.org/abs/2410.19469 |