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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17080 |
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| _version_ | 1866909537011761152 |
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| author | Schooltink, Willem Zennaro, Fabio Massimo |
| author_facet | Schooltink, Willem Zennaro, Fabio Massimo |
| contents | Causal abstractions allow us to relate causal models on different levels of granularity. To ensure that the models agree on cause and effect, frameworks for causal abstractions define notions of consistency. Two distinct methods for causal abstraction are common in the literature: (i) graphical abstractions, such as Cluster DAGs, which relate models on a structural level, and (ii) functional abstractions, like $α$-abstractions, which relate models by maps between variables and their ranges. In this paper we will align the notions of graphical and functional consistency and show an equivalence between the class of Cluster DAGs, consistent $α$-abstractions with the range of abstracted variables mapped bijectively, and constructive $τ$-abstractions. Furthermore, we extend this alignment and the expressivity of graphical abstractions by introducing Partial Cluster DAGs. Our results provide a rigorous bridge between the functional and graphical frameworks and allow for adoption and transfer of results between them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17080 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aligning Graphical and Functional Causal Abstractions Schooltink, Willem Zennaro, Fabio Massimo Artificial Intelligence Causal abstractions allow us to relate causal models on different levels of granularity. To ensure that the models agree on cause and effect, frameworks for causal abstractions define notions of consistency. Two distinct methods for causal abstraction are common in the literature: (i) graphical abstractions, such as Cluster DAGs, which relate models on a structural level, and (ii) functional abstractions, like $α$-abstractions, which relate models by maps between variables and their ranges. In this paper we will align the notions of graphical and functional consistency and show an equivalence between the class of Cluster DAGs, consistent $α$-abstractions with the range of abstracted variables mapped bijectively, and constructive $τ$-abstractions. Furthermore, we extend this alignment and the expressivity of graphical abstractions by introducing Partial Cluster DAGs. Our results provide a rigorous bridge between the functional and graphical frameworks and allow for adoption and transfer of results between them. |
| title | Aligning Graphical and Functional Causal Abstractions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.17080 |