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The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy

Emadi, Seyed Morteza
arXiv (All) · Papers
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artificial-intelligenceinformation-theorymachine-learning
Pearl's causal hierarchy shows that observational, interventional, and counterfactual queries are qualitatively distinct. We ask a quantitative version of this question: how many additional bits are needed to specify higher-rung causal answers once lower-rung answers are known? We formalize this via
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