Automatic debiased machine learning for covariate shifts

SUMMARY We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One such parameter is the average effect of a policy that alters the covariate distribution, such as a treatment modifying surrogate covariates used to predict long-term outcomes. Another example is the average treatment effect for a population with a shifted covariate distribution. We propose a debiased machine learning method to estimate a broad class of these parameters in a statistically reliable and automatic manner. Our method eliminates regularization biases arising from the use of machine learning tools in high-dimensional settings, and relies solely on the parameter’s defining formula. It employs data fusion by combining samples of target and training data to eliminate biases. We give asymptotic theory that allows the sample sizes of training and target data to grow at different rates. Computational experiments and an empirical study on the impact of minimum wage increases on teen employment, using the difference-in-differences framework with unconfoundedness, demonstrate the effectiveness of our method.