Multi-Level Causal Embeddings
#causal embeddings #artificial intelligence #machine learning #causal modeling #abstraction #multi-resolution #data merging #knowledge representation
📌 Key Takeaways
- New framework for mapping multiple detailed causal models into coarser models
- Causal embeddings presented as generalization of abstraction
- Research addresses both statistical and causal marginal problems
- Practical applications in merging datasets from different model representations
📖 Full Retelling
Computer scientists Willem Schooltink and Fabio Massimo Zennaro published their new research paper 'Multi-Level Causal Embeddings' on the arXiv preprint server on February 25, 2026, introducing a novel framework that enables mapping multiple detailed causal models into sub-systems of a coarser model while preserving cause-and-effect relationships. The research, categorized under Artificial Intelligence and Machine Learning, presents causal embeddings as a generalization of abstraction, allowing for the coarsening of models such that causal relationships remain intact. The authors develop a generalized notion of consistency and define a multi-resolution marginal problem to demonstrate the relevance of their framework for both statistical and causal marginal problems. This approach also shows practical applications in merging datasets from models with different representations, potentially advancing how artificial intelligence systems understand and process complex causal relationships. This contribution to the field of artificial intelligence addresses a fundamental challenge in causal modeling by providing a systematic way to handle multiple levels of abstraction simultaneously. By establishing connections between different models while preserving their causal structures, the research opens new possibilities for more efficient knowledge representation and reasoning in AI systems.
🏷️ Themes
Artificial Intelligence, Causal Modeling, Knowledge Representation
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22287 [Submitted on 25 Feb 2026] Title: Multi-Level Causal Embeddings Authors: Willem Schooltink , Fabio Massimo Zennaro View a PDF of the paper titled Multi-Level Causal Embeddings, by Willem Schooltink and 1 other authors View PDF HTML Abstract: 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. Subjects: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG) Cite as: arXiv:2602.22287 [cs.AI] (or arXiv:2602.22287v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22287 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Willem Schooltink [ view email ] [v1] Wed, 25 Feb 2026 14:14:13 UTC (62 KB) Full-text links: Access Paper: View a PDF of the paper titled Multi-Level Causal Embeddings, by Willem Schooltink and 1 other authors View PDF HTML TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What...
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