# The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data

arXiv: https://arxiv.org/abs/2312.07837 · HTML: https://mlsanity.com/paper/2312.07837/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [Benchmarking Differentially Private Synthetic Data Generation Algorithms](https://mlsanity.com/paper/2112.09238/) (2021)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks](https://mlsanity.com/paper/2109.00666/) (2021)
- [Synthetic data, real errors: how (not) to publish and use synthetic data](https://mlsanity.com/paper/2305.09235/) (2023)
- [Foundations of Bayesian Learning from Synthetic Data](https://mlsanity.com/paper/2011.08299/) (2020)
- [Assumption-lean inference for generalised linear model parameters](https://mlsanity.com/paper/2006.08402/) (2020)
- [Noise-Aware Statistical Inference with Differentially Private Synthetic Data](https://mlsanity.com/paper/2205.14485/) (2022)
- [30 Years of Synthetic Data](https://mlsanity.com/paper/2304.02107/) (2023)
- [Mitigating Statistical Bias within Differentially Private Synthetic Data](https://mlsanity.com/paper/2108.10934/) (2021)

## Built on by (most cited first, 6 of 6)

- [General Synthetic-Powered Inference](https://mlsanity.com/paper/2509.20345/) (2025)
- [High-Dimensional Statistics: Reflections on Progress and Open Problems](https://mlsanity.com/paper/2605.05076/) (2026)
- [Debiasing Synthetic Data Generated by Deep Generative Models](https://mlsanity.com/paper/2411.04216/) (2024)
- [Harnessing Synthetic Data from Generative AI for Statistical Inference](https://mlsanity.com/paper/2603.05396/) (2026)
- [Valid Inference with Synthetic Data via Task Exchangeability](https://mlsanity.com/paper/2606.13629/) (2026)
- [ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets](https://mlsanity.com/paper/2608.18643/) (2026)

## Abstract (arXiv)

Recent advances in generative models facilitate the creation of synthetic data to be made available for research in privacy-sensitive contexts. However, the analysis of synthetic data raises a unique set of methodological challenges. In this work, we highlight the importance of inferential utility and provide empirical evidence against naive inference from synthetic data, whereby synthetic data are treated as if they were actually observed. Before publishing synthetic data, it is essential to develop statistical inference tools for such data. By means of a simulation study, we show that the rate of false-positive findings (type 1 error) will be unacceptably high, even when the estimates are unbiased. Despite the use of a previously proposed correction factor, this problem persists for deep generative models, in part due to slower convergence of estimators and resulting underestimation of the true standard error. We further demonstrate our findings through a case study.