[2509.11950] TabStruct: Measuring Structural Fidelity of Tabular Data

Computer Science > Machine Learning arXiv:2509.11950 (cs) [Submitted on 15 Sep 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:TabStruct: Measuring Structural Fidelity of Tabular Data Authors:Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik View a PDF of the paper titled TabStruct: Measuring Structural Fidelity of Tabular Data, by Xiangjian Jiang and 2 other authors View PDF HTML (experimental) Abstract:Evaluating tabular generators remains a challenging problem, as the unique causal structural prior of heterogeneous tabular data does not lend itself to intuitive human inspection. Recent work has introduced structural fidelity as a tabular-specific evaluation dimension to assess whether synthetic data complies with the causal structures of real data. However, existing benchmarks often neglect the interplay between structural fidelity and conventional evaluation dimensions, thus failing to provide a holistic understanding of model performance. Moreover, they are typically limited to toy datasets, as quantifying existing structural fidelity metrics requires access to ground-truth causal structures, which are rarely available for real-world datasets. In this paper, we propose a novel evaluation framework that jointly considers structural fidelity and conventional evaluation dimensions. We introduce a new evaluation metric, $\textbf{global utility}$, which enables the assessment of structural fidelity even in the absence of ground-truth causal structures. In ad...