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Smoothness in treatment effects unlocks near-optimal policy learning in high-dimensional, low-signal digital experimentation.
Classical hardness measures miss an entire dimension of bandit exploration: best-arm identification complexity is fundamentally dictated by the Shannon entropy of gap distributions across dyadic scales.
