Tabular synthesis models remain ineffective at capturing complex dependencies, and the quality of synthetic data is still insufficient for comprehensive downstream tasks, such as prediction under distribution shifts, automated decision-making, and cross-table understanding. A major challenge is the lack of prior knowledge about underlying structures and high-order relationships in tabular data. We argue that a systematic evaluation on high-order structural information for tabular data synthesis is the first step towards solving the problem. In this paper, we introduce high-order structural causal information as natural prior knowledge and provide a benchmark framework for the evaluation of tabular synthesis models. The framework allows us to generate benchmark datasets with a flexible range of data generation processes and to train tabular synthesis models using these datasets for further evaluation. We propose multiple benchmark tasks, high-order metrics, and causal inference tasks as downstream tasks for evaluating the quality of synthetic data generated by the trained models. Our experiments demonstrate to leverage the benchmark framework for evaluating the model capability of capturing high-order structural causal information. Furthermore, our benchmarking results provide an initial assessment of state-of-the-art tabular synthesis models. They have clearly revealed significant gaps between ideal and actual performance and how baseline methods differ. Our benchmark framework is available at URL https://github.com/TURuibo/CauTabBench.

翻译：表格合成模型在捕捉复杂依赖关系方面仍然效果不佳，合成数据的质量尚不足以支撑全面的下游任务，例如分布偏移下的预测、自动化决策以及跨表格理解。一个主要挑战在于缺乏对表格数据底层结构和高阶关系的先验知识。我们认为，对表格数据合成的高阶结构信息进行系统评估是解决该问题的第一步。本文引入高阶结构因果信息作为自然先验知识，并为表格合成模型的评估提供了一个基准框架。该框架允许我们生成具有灵活数据生成过程的基准数据集，并利用这些数据集训练表格合成模型以进行进一步评估。我们提出了多项基准任务、高阶度量指标以及因果推理任务作为下游任务，用于评估训练模型生成的合成数据质量。实验表明，该基准框架能够有效评估模型捕捉高阶结构因果信息的能力。此外，我们的基准测试结果对当前最先进的表格合成模型进行了初步评估，清晰揭示了理想性能与实际性能之间的显著差距以及基线方法之间的差异。我们的基准框架已在 https://github.com/TURuibo/CauTabBench 公开提供。