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Scaling and post-hoc interpretability hit a hard wall in agentic AI because models cannot recover causal invariances that observational interaction data never contained in the first place.
Causal priors dramatically improve the robustness of automated feature engineering, reducing performance drops by 4x under distribution shift compared to non-causal methods.
