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When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions

Zaghw, Karim et al.
arXiv (All) · Papers
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artificial-intelligencemachine-learning
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 i
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