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About this paper
Fabio Massimo Zennaro
Fabio Massimo Zennaro's review systematically analyzes existing definitions of abstraction in Structural Causal Models, categorizing them by structural and distributional layers and their formal properties, to provide a unified framework for understanding model simplification and guiding causal representation learning.
Key insights
- SCM abstractions can be more effectively understood and designed by explicitly considering their impact on the causal graph structure and on the variable outcome distributions as distinct layers.
- Enforcing or relaxing specific formal properties on either the structural or distributional layer dictates the types of transformations allowed, such as node coarsening, edge dropping, or embedding.
- The term 'coarsening' refers to distinct phenomena on the structural layer (reducing observables) versus the distributional layer (reducing outcome resolution), which can occur independently.
Summary by alphaXiv.
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