Learning Robust Models Using The Principle of Independent Causal Mechanisms
This work addresses the critical issue of distribution shift in supervised learning, offering a method to improve model robustness, though it builds incrementally on existing causal principles.
The paper tackles the problem of model robustness under data distribution shift by proposing a gradient-based learning framework derived from the independent causal mechanisms principle, showing that it enables neural networks to focus on invariant relations and generalize better to unseen scenarios where standard models fail.
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.