Counterfactual Augmentation
Counterfactual augmentation is a data augmentation technique that generates synthetic data points by altering existing instances to represent "what if" scenarios, addressing issues like class imbalance, bias, and robustness in machine learning models. Current research focuses on applying this technique across diverse domains, including text classification, image analysis (especially medical imaging), and knowledge graph completion, often leveraging graph neural networks or transformer-based architectures to generate and integrate these counterfactual samples. This approach holds significant promise for improving model performance, fairness, and interpretability in various applications by mitigating the limitations of skewed or insufficient training data.