用因果感知扩散模型生成混合类型表格数据，提升真实性和多样性。
CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation
- 基于扩散模型设计，融合因果先验增强变量关系建模
- 在7个数据集上均超越基线方法，生成数据更接近真实分布
- 适合需要隐私保护的高质量表格数据合成场景
训练数据是生成式AI中至关重要的组成部分，但高质量数据获取困难，尤其受数据隐私限制。数据合成已成为主流解决方案，在图像、音频和视频领域表现优异。然而，生成包含数值与类别特征的混合类型表格数据仍面临挑战，主要源于数据类型的异质性、复杂变量间关系及复杂的列分布结构。本文提出CausalDiffTab，一种专为混合类型表格数据设计的扩散生成模型，具备更强的变量交互建模能力。我们进一步提出基于分层先验融合原理的混合自适应因果正则化方法，动态调节因果约束权重，在不牺牲生成能力的前提下提升性能。在7个数据集上的综合实验表明，CausalDiffTab在所有指标上均优于基线方法。代码已公开于：https://github.com/Godz-z/CausalDiffTab。
原文摘要 · Abstract (English)
Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issues presenting a significant hurdle. To address the need for high-quality data. Synthesize data has emerged as a mainstream solution, demonstrating impressive performance in areas such as images, audio, and video. Generating mixed-type data, especially high-quality tabular data, still faces significant challenges. These primarily include its inherent heterogeneous data types, complex inter-variable relationships, and intricate column-wise distributions. In this paper, we introduce CausalDiffTab, a diffusion model-based generative model specifically designed to handle mixed tabular data containing both numerical and categorical features, while being more flexible in capturing complex interactions among variables. We further propose a hybrid adaptive causal regularization method based on the principle of Hierarchical Prior Fusion. This approach adaptively controls the weight of causal regularization, enhancing the model's performance without compromising its generative capabilities. Comprehensive experiments conducted on seven datasets demonstrate that CausalDiffTab outperforms baseline methods across all metrics. Our code is publicly available at: https://github.com/Godz-z/CausalDiffTab.
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