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stat.ME2015★ 5 cited
Blocking estimators and inference under the Neyman-Rubin model
Michael J. Higgins, Fredrik Sävje, Jasjeet S. Sekhon
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 numbe…
math.ST2015★ 183 cited
Lasso adjustments of treatment effect estimates in randomized experiments
Adam Bloniarz, Hanzhong Liu, Cun-Hui Zhang +2
We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to an…