  In general, graph representation learning methods assume that the test and
train data come from the same distribution. In this work we consider an
underexplored area of an otherwise rapidly developing field of graph
representation learning: The task of out-of-distribution (OOD) graph
classification, where train and test data have different distributions, with
test data unavailable during training. Our work shows it is possible to use a
causal model to learn approximately invariant representations that better
extrapolate between train and test data. Finally, we conclude with synthetic
and real-world dataset experiments showcasing the benefits of representations
that are invariant to train/test distribution shifts.
