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.

翻译：本文研究了协变量偏移下参数机器学习中的偏差问题。协变量偏移是指训练阶段与部署阶段的输入特征分布发生变化的情况。机器学习中的正则化与模型选择会导致许多参数估计产生偏差。本文提出了一种面向协变量偏移的自动去偏机器学习方法，以纠正此类偏差。该方法利用去偏机器学习领域的最新进展，在存在协变量偏移时对策略参数和因果参数的估计量进行去偏。其去偏过程是自动的，仅依赖于目标参数，无需已知偏差的具体形式。我们证明了随着样本量增大，该估计量具有渐近正态性。最后，通过蒙特卡洛模拟在回归问题上验证了所提方法的有效性。