  Data augmentation is one of the most successful techniques to improve the
classification accuracy of machine learning models in computer vision. However,
applying data augmentation to tabular data is a challenging problem since it is
hard to generate synthetic samples with labels. In this paper, we propose an
efficient classifier with a novel data augmentation technique for tabular data.
Our method called CCRAL combines causal reasoning to learn counterfactual
samples for the original training samples and active learning to select useful
counterfactual samples based on a region of uncertainty. By doing this, our
method can maximize our model's generalization on the unseen testing data. We
validate our method analytically, and compare with the standard baselines. Our
experimental results highlight that CCRAL achieves significantly better
performance than those of the baselines across several real-world tabular
datasets in terms of accuracy and AUC. Data and source code are available at:
https://github.com/nphdang/CCRAL.
