Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical results verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. The \emph{fake invariance} severely endangers OOD generalization since the trustful objective can not be diagnosed and existing causal surgeries are invalid to rectify. In this paper, we review a IRL family (InvRat) under the Partially and Fully Informative Invariant Feature Structural Causal Models (PIIF SCM /FIIF SCM) respectively, to certify their weaknesses in representing fake invariant features, then, unify their causal diagrams to propose ReStructured SCM (RS-SCM). RS-SCM can ideally rebuild the spurious and the fake invariant features simultaneously. Given this, we further develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects. It can be easily implemented by a small feature selection subnet introduced in the IRL family, which is alternatively optimized to achieve our goal. Experiments verified the superiority of our approach to fight against the fake invariant issue across a variety of OOD generalization benchmarks.

翻译：不变表示学习（IRL）鼓励从不变因果特征到标签的预测去除了环境混淆效应，推动了分布外（OOD）泛化的技术路线发展。尽管备受关注，但近期理论结果证实，IRL恢复的部分因果特征仅在训练环境中表现为域不变性，而在未见域中失效。这种“虚假不变性”严重危害OOD泛化，因为其可信目标无法被诊断，且现有因果干预手段无法修正。本文分别基于部分信息与完全信息不变特征结构因果模型（PIIF SCM/FIIF SCM）审视IRL家族（InvRat），验证其在表征虚假不变特征上的局限性，进而统一其因果图提出重构结构因果模型（RS-SCM）。RS-SCM能够理想地同时重建虚假特征与虚假不变特征。基于此，我们进一步提出一种基于RS-SCM的条件互信息方法，对虚假与虚假不变效应进行严格修正。该方法可通过在IRL家族中引入一个小型特征选择子网络轻松实现，通过交替优化达成目标。实验证明，该方法在多个OOD泛化基准上对抗虚假不变问题具有显著优势。