Automatic debiased machine learning for covariate shifts

Abstract

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 that modifies 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 bias 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 bias. We give asymptotic theory that allows the sample sizes of the training and target datato grow at different rates. Computational experiments and an empirical study of the impact ofminimum-wage increases on teen employment, using the difference-in-differences framework with unconfoundedness, demonstrate the effectiveness of our method.