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Blocking estimators and inference under the Neyman-Rubin model
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We derive the variances of estimators for sample average treatment effects under the Neyman-Rubin potential outcomes model for arbitrary blocking assignments and an arbitrary number of treatments.
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Cited by 3 Pith papers
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Gaussian comparison above the median
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Regression-adjusted average treatment effect estimates in stratified randomized experiments
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