Multi-Level Causal Embeddings
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911473702273024 |
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| author | Schooltink, Willem Zennaro, Fabio Massimo |
| author_facet | Schooltink, Willem Zennaro, Fabio Massimo |
| contents | Abstractions of causal models allow for the coarsening of models such that relations of cause and effect are preserved. Whereas abstractions focus on the relation between two models, in this paper we study a framework for causal embeddings which enable multiple detailed models to be mapped into sub-systems of a coarser causal model. We define causal embeddings as a generalization of abstraction, and present a generalized notion of consistency. By defining a multi-resolution marginal problem, we showcase the relevance of causal embeddings for both the statistical marginal problem and the causal marginal problem; furthermore, we illustrate its practical use in merging datasets coming from models with different representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22287 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Level Causal Embeddings Schooltink, Willem Zennaro, Fabio Massimo Artificial Intelligence Machine Learning Abstractions of causal models allow for the coarsening of models such that relations of cause and effect are preserved. Whereas abstractions focus on the relation between two models, in this paper we study a framework for causal embeddings which enable multiple detailed models to be mapped into sub-systems of a coarser causal model. We define causal embeddings as a generalization of abstraction, and present a generalized notion of consistency. By defining a multi-resolution marginal problem, we showcase the relevance of causal embeddings for both the statistical marginal problem and the causal marginal problem; furthermore, we illustrate its practical use in merging datasets coming from models with different representations. |
| title | Multi-Level Causal Embeddings |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.22287 |