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 non-parametric regression adjustments with machine learning methods such as random forests or neural networks.

To appear in the Proceedings of the National Academy of Sciences. The present draft does not reflect final copyediting by the PNAS staff

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Cited by in corpus (13)

• Quasi-Oracle Estimation of Heterogeneous Treatment Effects
• Regression adjustment in completely randomized experiments with a diverging number of covariates
• A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond
• Covariate-adjusted Fisher randomization tests for the average treatment effect
• Machine Learning for Variance Reduction in Online Experiments
• The Generalized Oaxaca-Blinder Estimator
• Causal Inference: A Missing Data Perspective
• Regression adjustments for estimating the global treatment effect in experiments with interference
• Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments
• The LOOP Estimator: Adjusting for Covariates in Randomized Experiments
• Rerandomization and Regression Adjustment
• On High Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes
• The P-LOOP Estimator: Covariate Adjustment for Paired Experiments