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 evaluate the method using a simulation.

翻译：本文研究了协变量偏移下机器学习参数估计中的偏差问题。协变量偏移指的是训练阶段与部署阶段输入特征分布发生变化的现象。与机器学习相关的正则化及模型选择会导致许多参数估计产生偏差。本文提出一种自动去偏机器学习方法，以纠正协变量偏移下的估计偏差。该方法利用去偏机器学习领域的前沿技术，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处理。该去偏过程具有自动性，仅依赖于目标参数，无需事先指定偏差的具体形式。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，通过仿真实验对方法进行了评估。