A Durbin-Levinson regularized estimator of high-dimensional autocovariance matrices

Abstract

In paired randomized experiments, individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners.We examine the use of regression adjustment to correct for persistent covariate imbalances after randomization, and present two regression-assisted estimators for the sample average treatment effect in paired experiments. Using the potential outcomes framework, we prove that these estimators are consistent for the sample average treatment effect under mild regularity conditions even if the regression model is improperly specified, and describe how asymptotically conservative confidence intervals can be constructed.We demonstrate that the variances of the regressionassisted estimators are no larger than that of the standard difference-in-means estimator asymptotically, and illustrate the proposed methods by simulation. The analysis does not require a superpopulation model, a constant treatment effect, or the truth of the regression model, and hence provides inference for the sample average treatment effect with the potential to increase power without unrealistic assumpt.