论文标题
稳定性和外部有效性的变形和因果正规化
Deconfounding and Causal Regularization for Stability and External Validity
论文作者
论文摘要
我们从统一的角度回顾了一些有关消除隐藏混杂和因果正规化的最新工作。我们描述了简单和用户友好的技术如何改善异质数据中的稳定性，可复制性和分布鲁棒性。从这个意义上讲，当数据生成分布正在发生变化时，我们为概念漂移的问题提供了其他想法。
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.
