Nonlinear Invariant Risk Minimization: A Causal Approach

Understand

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 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.