通过反事实残差生成新数据，提升小样本回归的准确性
Counterfactual Residual Data Augmentation for Regression
- 基于模型残差的稳定性，生成真实且多样化的合成数据
- 使MLP和XGBoost的均方误差分别降低22.9%和6.4%
- 无需额外真实数据，适用于各类回归模型
现实世界中的回归任务常面临训练样本少、采集成本高和观测噪声大的问题。受视觉与自然语言领域数据增强的启发，我们提出一种针对表格回归的反事实残差数据增强（CRDA）方法。核心思想是：当回归器建模了数据的系统性成分后，剩余噪声可视为在特定特征微小扰动下保持稳定的不变残差。利用该残差不变性，生成新但真实的训练样本，有效扩充数据集而不依赖额外真实数据。该方法与模型无关，可广泛应用于各类回归器。在多个基准数据集上的实验表明，平均而言，CRDA使MLP回归器的均方误差（MSE）降低22.9%，XGBoost回归器的MSE降低6.4%。相较于现有先进数据生成与增强技术，CRDA在降低MSE方面始终表现更优。通过引入有原则的反事实变化，该方法为噪声敏感、样本稀少的回归场景提供了一种简单高效的解决方案。
原文摘要 · Abstract (English)
Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augmentation in vision and language, we propose a novel Counterfactual Residual Data Augmentation (CRDA) technique for tabular regression. Our key insight is that once a regressor has modeled the systematic component of the data, the remaining noise can be viewed as an invariant residual that remains stable under small perturbations of carefully selected features. We exploit this residual invariance to generate new, yet realistic, training samples, effectively expanding the dataset without requiring additional real data. Our method is model-agnostic and readily applicable to various types of regressors. In experiments across datasets from a variety of benchmark repositories, on average, CRDA reduces an MLP Regressor's MSE by 22.9% and an XGBoost Regressor's MSE by 6.4%. When compared to existing state-of-the-art data generators and augmentation techniques, CRDA consistently outperforms in MSE reduction. By adding principled counterfactual variations to the training data, our method offers a simple and efficient remedy for noise-prone, small-sample regression settings.
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