Observational causality by states and interaction type for scientific discovery
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916876946243584 |
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| author | Martínez-Sánchez, Álvaro Lozano-Durán, Adrián |
| author_facet | Martínez-Sánchez, Álvaro Lozano-Durán, Adrián |
| contents | Causality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and direction of causality vary with the value of the interacting variables. In this work, we introduce a state-aware causal inference method that quantifies causality in terms of information gain about future states. The effectiveness of the proposed approach stems from two key features: its ability to characterize causal influence as a function of system state, and its capacity to distinguish between redundant and synergistic interactions. The method is validated across a range of benchmark cases in which the direction and strength of causality evolve in a prescribed manner with the state of the system. We further demonstrate the applicability of our approach in two real scenarios: the interaction between motions across scales in a turbulent boundary layer, and the Walker circulation phenomenon in tropical Pacific climate dynamics. Our results show that, without accounting for state-dependent causality as well as redundant and synergistic effects, traditional approaches to causal inference may lead to incomplete or misleading conclusions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Observational causality by states and interaction type for scientific discovery Martínez-Sánchez, Álvaro Lozano-Durán, Adrián Data Analysis, Statistics and Probability Fluid Dynamics Causality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and direction of causality vary with the value of the interacting variables. In this work, we introduce a state-aware causal inference method that quantifies causality in terms of information gain about future states. The effectiveness of the proposed approach stems from two key features: its ability to characterize causal influence as a function of system state, and its capacity to distinguish between redundant and synergistic interactions. The method is validated across a range of benchmark cases in which the direction and strength of causality evolve in a prescribed manner with the state of the system. We further demonstrate the applicability of our approach in two real scenarios: the interaction between motions across scales in a turbulent boundary layer, and the Walker circulation phenomenon in tropical Pacific climate dynamics. Our results show that, without accounting for state-dependent causality as well as redundant and synergistic effects, traditional approaches to causal inference may lead to incomplete or misleading conclusions. |
| title | Observational causality by states and interaction type for scientific discovery |
| topic | Data Analysis, Statistics and Probability Fluid Dynamics |
| url | https://arxiv.org/abs/2505.10878 |