The LOOP Estimator: Adjusting for Covariates in Randomized Experiments

Abstract

In this article, we propose the "leave-one-out potential outcomes" estimator. We leave out each observation and then impute that observation's treatment and control potential outcomes using a prediction algorithm such as a random forest. In addition to allowing for automatic variable selection, this estimator is unbiased under the Neyman-Rubin model, generally performs at least as well as the unadjusted estimator, and the experimental randomization largely justifies the statistical assumptions made.