# TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

arXiv: https://arxiv.org/abs/2207.05295 · HTML: https://mlsanity.com/paper/2207.05295/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://mlsanity.com/paper/2204.00401/) (2022)
- [TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks](https://mlsanity.com/paper/2109.00666/) (2021)
- [Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data](https://mlsanity.com/paper/1412.2282/) (2014)
- [Bayesian mixture modeling for multivariate conditional distributions](https://mlsanity.com/paper/1606.04457/) (2016)

## Built on by (most cited first, 27 of 27)

- [A Survey on Data Synthesis and Augmentation for Large Language Models](https://mlsanity.com/paper/2410.12896/) (2024)
- [Systematic Assessment of Tabular Data Synthesis](https://mlsanity.com/paper/2402.06806/) (2024)
- [Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare](https://mlsanity.com/paper/2401.13716/) (2024)
- [A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://mlsanity.com/paper/2510.25817/) (2025)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [Structured Evaluation of Synthetic Tabular Data](https://mlsanity.com/paper/2403.10424/) (2024)
- [Struct-Bench: A Benchmark for Differentially Private Structured Text Generation](https://mlsanity.com/paper/2509.10696/) (2025)
- [Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework](https://mlsanity.com/paper/2504.01908/) (2025)
- [An evaluation framework for synthetic data generation models](https://mlsanity.com/paper/2404.08866/) (2024)
- [Evaluating Differentially Private Generation of Domain-Specific Text](https://mlsanity.com/paper/2508.20452/) (2025)
- [MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime](https://mlsanity.com/paper/2307.07997/) (2023)
- [Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead](https://mlsanity.com/paper/2503.20846/) (2025)
- [Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data](https://mlsanity.com/paper/2312.12216/) (2023)
- [Fairness Testing through Extreme Value Theory](https://mlsanity.com/paper/2501.11597/) (2025)
- [Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets](https://mlsanity.com/paper/2211.10760/) (2022)
- [SHAP Distance: An Explainability-Aware Metric for Evaluating the Semantic Fidelity of Synthetic Tabular Data](https://mlsanity.com/paper/2511.17590/) (2025)
- [Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators](https://mlsanity.com/paper/2405.07196/) (2024)
- [Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints](https://mlsanity.com/paper/2505.05019/) (2025)
- [GEM-T: Generative Tabular Data via Fitting Moments](https://mlsanity.com/paper/2509.17752/) (2025)
- [Measuring LLM Sensitivity in Transformer-based Tabular Data Synthesis](https://mlsanity.com/paper/2509.20768/) (2025)
- [Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen](https://mlsanity.com/paper/2511.16373/) (2025)
- [Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models](https://mlsanity.com/paper/2602.15270/) (2026)
- [Evaluating quality in synthetic data generation for large tabular health datasets](https://mlsanity.com/paper/2604.15961/) (2026)
- [Declarative Outcome-Conformant Synthesis: Exact, Closed-Form Specification Satisfaction and a Conformance Benchmark](https://mlsanity.com/paper/2606.08736/) (2026)
- [PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization](https://mlsanity.com/paper/2606.18518/) (2026)
- [Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation](https://mlsanity.com/paper/2610.06854/) (2026)

## Abstract (arXiv)

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, generative adversarial networks (GANs), and variational auto-encoder (VAEs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. The proposed metric assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for ``high quality'' synthetic data. Being a single score metric and having an implicit bound, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models. We also give a comparative analysis between TabSynDex and existing synthetic tabular data evaluation metrics. This shows the effectiveness and universality of our metric over the existing metrics. Source Code: urlhttps://github.com/vikram2000b/tabsyndex