  Causal graphs (CGs) are compact representations of the knowledge of the data
generating processes behind the data distributions. When a CG is available,
e.g., from the domain knowledge, we can infer the conditional independence (CI)
relations that should hold in the data distribution. However, it is not
straightforward how to incorporate this knowledge into predictive modeling. In
this work, we propose a model-agnostic data augmentation method that allows us
to exploit the prior knowledge of the CI encoded in a CG for supervised machine
learning. We theoretically justify the proposed method by providing an excess
risk bound indicating that the proposed method suppresses overfitting by
reducing the apparent complexity of the predictor hypothesis class. Using
real-world data with CGs provided by domain experts, we experimentally show
that the proposed method is effective in improving the prediction accuracy,
especially in the small-data regime.
