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Causal Confusion
A failure mode in imitation learning where the policy learns to rely on spurious correlations in the demonstration data rather than the true causal features. For example, a driving policy might learn that the brake light indicator predicts stopping, rather than learning to stop based on traffic conditions. Causal confusion often manifests as policies that work in-distribution but fail unpredictably on new scenarios.