High-dimensional regression adjustments in randomized experiments
This provides a general framework for valid inference in high-dimensional causal inference settings, extending applicability to flexible models while maintaining statistical guarantees.
The paper tackles treatment effect estimation in randomized experiments with high-dimensional covariates, showing that risk-consistent regression adjustments yield efficient average treatment effect estimates and proposing cross-estimation for finite-sample-unbiased estimates. The method works with various regression techniques like lasso, elastic net, and machine learning methods such as random forests or neural networks.
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.