  Machine learning models trained with purely observational data and the
principle of empirical risk minimization \citep{vapnik_principles_1992} can
fail to generalize to unseen domains. In this paper, we focus on the case where
the problem arises through spurious correlation between the observed domains
and the actual task labels. We find that many domain generalization methods do
not explicitly take this spurious correlation into account. Instead, especially
in more application-oriented research areas like medical imaging or robotics,
data augmentation techniques that are based on heuristics are used to learn
domain invariant features. To bridge the gap between theory and practice, we
develop a causal perspective on the problem of domain generalization. We argue
that causal concepts can be used to explain the success of data augmentation by
describing how they can weaken the spurious correlation between the observed
domains and the task labels. We demonstrate that data augmentation can serve as
a tool for simulating interventional data. We use these theoretical insights to
derive a simple algorithm that is able to select data augmentation techniques
that will lead to better domain generalization.
