title: High-dimensional regression adjustments in randomized experiments.
abstract: We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.
authors: Department of Statistics, Stanford University, Stanford, CA 94305; swager@stanford.edu tibs@stanford.edu.|Operations, Information & Technology, Stanford Graduate School of Business, Stanford University, Stanford, CA 94305.
authors: Stefan
authors: S
authors: Wager
authors: Department of Statistics, Stanford University, Stanford, CA 94305.
authors: Wenfei
authors: W
authors: Du
authors: Department of Statistics, Stanford University, Stanford, CA 94305.
authors: Jonathan
authors: J
authors: Taylor
authors: Department of Statistics, Stanford University, Stanford, CA 94305; swager@stanford.edu tibs@stanford.edu.|Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
authors: Robert J
authors: RJ
authors: Tibshirani
keywords: high-dimensional confounders; randomized trials; regression adjustment