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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.12411 |
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| _version_ | 1866916738721906688 |
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| author | Martínez-Sánchez, Álvaro Arranz, Gonzalo Lozano-Durán, Adrián |
| author_facet | Martínez-Sánchez, Álvaro Arranz, Gonzalo Lozano-Durán, Adrián |
| contents | Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12411 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decomposing causality into its synergistic, unique, and redundant components Martínez-Sánchez, Álvaro Arranz, Gonzalo Lozano-Durán, Adrián Data Analysis, Statistics and Probability Fluid Dynamics Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods. |
| title | Decomposing causality into its synergistic, unique, and redundant components |
| topic | Data Analysis, Statistics and Probability Fluid Dynamics |
| url | https://arxiv.org/abs/2405.12411 |