  Deep learning models tend not to be out-of-distribution robust primarily due
to their reliance on spurious features to solve the task. Counterfactual data
augmentations provide a general way of (approximately) achieving
representations that are counterfactual-invariant to spurious features, a
requirement for out-of-distribution (OOD) robustness. In this work, we show
that counterfactual data augmentations may not achieve the desired
counterfactual-invariance if the augmentation is performed by a
context-guessing machine, an abstract machine that guesses the most-likely
context of a given input. We theoretically analyze the invariance imposed by
such counterfactual data augmentations and describe an exemplar NLP task where
counterfactual data augmentation by a context-guessing machine does not lead to
robust OOD classifiers.
