Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output. To address the problem, we propose a Causal Semantic Generative model (CSG) based on causality to model the two factors separately, and learn it on a single training domain for prediction without (OOD generalization) or with (domain adaptation) unsupervised data in a test domain. We prove that CSG identifies the semantic factor on the training domain, and the invariance principle of causality subsequently guarantees the boundedness of OOD generalization error and the success of adaptation. We design learning methods for both effective learning and easy prediction, by leveraging the graphical structure of CSG. Empirical study demonstrates the effect of our methods to improve test accuracy for OOD generalization and domain adaptation.

通过提出基于因果推理的因果语义生成模型（CSG），并利用因果不变性原则，我们开发了一种用于从单个培训域进行OOD预测的方法，该方法将语义因素和变化因素分别建模，从而解决了传统的监督学习方法对OOD例子的敏感性问题，并且在实证研究中表现出比现有方法更好的OOD性能。

学习因果语义表征进行未知分布预测