用结构因果模型生成跨表关联的合成表格数据
Generating Synthetic Relational Tabular Data via Structural Causal Models
- 基于结构因果模型构建跨表数据生成框架
- 可生成具有复杂依赖关系的真实感合成数据
- 适合研究表格数据生成与因果建模的学者
近年来，合成表格数据生成受到越来越多关注，尤其是表格领域基础模型的兴起。TabPFN（Hollmann等，2025）的成功表明，大量源自结构因果模型（SCMs）的合成表格数据在训练强大表格基础模型中起关键作用。然而，大多数真实世界表格数据以多张互相关联的表形式存在，当前生成方法未能充分应对这种关系结构。本文扩展了基于SCM的方法，提出一种新框架，能够生成包含跨表因果关系的逼真合成关系型表格数据。实验表明，该框架可构建具有复杂表间依赖关系的数据集，模拟真实场景。
原文摘要 · Abstract (English)
Synthetic tabular data generation has received increasing attention in recent years, particularly with the emergence of foundation models for tabular data. The breakthrough success of TabPFN (Hollmann et al.,2025), which leverages vast quantities of synthetic tabular datasets derived from structural causal models (SCMs), demonstrates the critical role synthetic data plays in developing powerful tabular foundation models. However, most real-world tabular data exists in relational formats spanning multiple interconnected tables - a structure not adequately addressed by current generation methods. In this work, we extend the SCM-based approach by developing a novel framework that generates realistic synthetic relational tabular data including causal relationships across tables. Our experiments confirm that this framework is able to construct relational datasets with complex inter-table dependencies mimicking real-world scenarios.
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