Balanced designs that balance covariates (especially blocking) are asymptotically variance-optimal for binary ATE under Neyman's nonparametric model, and a CMH-based variance estimator is finite-sample conservative and asymptotically tight under local alternatives.
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Optimal Designs with Robust Inference for Binary Treatment Effects
Balanced designs that balance covariates (especially blocking) are asymptotically variance-optimal for binary ATE under Neyman's nonparametric model, and a CMH-based variance estimator is finite-sample conservative and asymptotically tight under local alternatives.