High-dimensional regression adjustments in randomized experiments

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...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2016-11, Vol.113 (45), p.12673-12678
Main Authors: Wager, Stefan, Du, Wenfei, Taylor, Jonathan, Tibshirani, Robert J.
Format: Article
Language:English
Subjects:
Artificial intelligence
Estimating techniques
Neural networks
Physical Sciences
Regression analysis
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1614732113