arXiv — cs.AI preprintsInternational16 October 2026
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions
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arXiv:2610.18155v2 Announce Type: replace-cross Abstract: Standard identifiability theory assumes one-dimensional latent factors, but learned representations can encode latent information across multidimensional blocks whose internal coordinates are not uniquely determined. When such blocks are used to represent and intervene on higher-level concepts, it is unclear whether their causal meaning survives the transformations left unresolved by block-identifiability. We connect block-identifiability to constructive causal abstraction. We show that block-diagonal invertible transformations followed
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