![[2602.22287] Multi-Level Causal Embeddings](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22287] Multi-Level Causal Embeddings
Summary
This article presents a framework for Multi-Level Causal Embeddings, which allows for the mapping of detailed causal models into coarser representations while preserving causal relationships.
Why It Matters
Understanding causal embeddings is crucial for advancing machine learning models that require the integration of diverse datasets. This framework can enhance the accuracy of statistical analyses and improve the merging of datasets from different representations, which is vital in various AI applications.
Key Takeaways
- Causal embeddings generalize the concept of model abstraction.
- The framework supports merging datasets with varying representations.
- It addresses both statistical and causal marginal problems.
- The approach enhances the preservation of cause-effect relationships.
- Practical applications include improved data integration in AI systems.
Computer Science > Artificial Intelligence arXiv:2602.22287 (cs) [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 (experimental) 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 Wi...
