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

arXiv:2312.07837v2 Announce Type: replace-cross
Abstract: 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 …

abstract advances analysis artificial arxiv challenges cs.lg data deal generative generative models highlight however importance privacy raises replace research set stat.ml synthetic synthetic data tabular type unique utility work