this post was submitted on 29 Oct 2023
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Machine Learning
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Causality is linked to disentanglement and sparse solutions. If you assume that there is a true causal representation, then there are ways to provably recover any permutation of such a representation given some assumptions. And being causal, the representation will also naturally be disentangled and sparse (a cat will clearly be separated from a dog).
See https://proceedings.mlr.press/v202/lachapelle23a/lachapelle23a.pdf.