We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts to the issue on concept drift, raised by Efron (2020), when the data generating distribution is changing.

中文翻译：

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稳定性和外部有效性的去混杂和因果正则化

我们从统一的角度回顾了最近关于消除隐藏的混淆和因果正则化的一些工作。我们描述了简单且用户友好的技术如何提高异构数据的稳定性、可复制性和分布鲁棒性。从这个意义上说，当数据生成分布发生变化时，我们对 Efron (2020) 提出的概念漂移问题提供了额外的想法。