Nonlinear Invariant Risk Minimization: A Causal Approach

• URL: http://arxiv.org/abs/2102.12353v1
• Date: Wed, 24 Feb 2021 15:38:41 GMT
• Title: Nonlinear Invariant Risk Minimization: A Causal Approach
• Authors: Chaochao Lu, Yuhuai Wu, Jo\'se Miguel Hern\'andez-Lobato, Bernhard Sch\"olkopf
• Abstract summary: We propose a learning paradigm that enables out-of-distribution generalization in the nonlinear setting. We show identifiability of the data representation up to very simple transformations. Extensive experiments on both synthetic and real-world datasets show that our approach significantly outperforms a variety of baseline methods.
• Score: 5.63479133344366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressing this, either explicitly or implicitly, attempted to find a data representation that has an invariant causal relationship with the target. This is done by leveraging a diverse set of training environments to reduce the effect of spurious features and build an invariant predictor. However, these methods have generalization guarantees only when both data representation and classifiers come from a linear model class. We propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution (OOD) generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: the prior over the data representation factorizes when conditioning on the target and the environment. Based on this, we show identifiability of the data representation up to very simple transformations. We also prove that all direct causes of the target can be fully discovered, which further enables us to obtain generalization guarantees in the nonlinear setting. Extensive experiments on both synthetic and real-world datasets show that our approach significantly outperforms a variety of baseline methods. Finally, in the concluding discussion, we further explore the aforementioned assumption and propose a general view, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes. The Agnostic Hypothesis can provide a unifying view of machine learning in terms of representation learning. More importantly, it can inspire a new direction to explore the general theory for identifying hidden causal factors, which is key to enabling the OOD generalization guarantees in machine learning.

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