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PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
Causal abstraction offers a principled framework for mechanistic interpretability, aligning a high-level causal model with low-level neural computation through interchange intervention analysis. Finding such an alignment, however, often requires fitting and evaluating separate learned mappings acros
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