Learning Robust Models using the Principle of Independent Causal Mechanisms

Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Koethe

Keywords: Causal Discovery, Principle of Independent Causal Mechanisms, Normalizing Flows, Domain Generalization

Abstract: Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, Peters et al. (2017)) can turn this weakness into an opportunity: one can take advantage of distribution shift between different environments during training in order to obtain more robust models. We propose a new gradient-based learning framework whose objective function is derived from the ICM principle. We show theoretically and experimentally that neural networks trained in this framework focus on relations remaining invariant across environments and ignore unstable ones. Moreover, we prove that the recovered stable relations correspond to the true causal mechanisms under certain conditions. In both regression and classification, the resulting models generalize well to unseen scenarios where traditionally trained models fail.

One-sentence Summary: A causal perspective on domain generalization turns distribution shift into an opportunity.

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