  In this paper we address the problem of bias in machine learning of
parameters following covariate shifts. Covariate shift occurs when the
distribution of input features change between the training and deployment
stages. Regularization and model selection associated with machine learning
biases many parameter estimates. In this paper, we propose an automatic
debiased machine learning approach to correct for this bias under covariate
shifts. The proposed approach leverages state-of-the-art techniques in debiased
machine learning to debias estimators of policy and causal parameters when
covariate shift is present. The debiasing is automatic in only relying on the
parameter of interest and not requiring the form of the form of the bias. We
show that our estimator is asymptotically normal as the sample size grows.
Finally, we demonstrate the proposed method on a regression problem using a
Monte-Carlo simulation.
