Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 113, Issue 45, Pages 12673-12678Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1614732113
Keywords
high-dimensional; confounders; randomized trials; regression adjustment
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Funding
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1608987] Funding Source: National Science Foundation
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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.
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