Debiasing synthetic data generated by deep generative models
- Author
- Alexander Decruyenaere (UGent) , Heidelinde Dehaene (UGent) , Paloma Rabaey (UGent) , Johan Decruyenaere (UGent) , Christiaan Polet (UGent) , Thomas Demeester (UGent) and Stijn Vansteelandt (UGent)
- Organization
- Project
- Abstract
- While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep generative models (DGMs) for synthetic data generation is known to induce considerable bias and imprecision into synthetic data analyses, compromising their inferential utility as opposed to original data analyses. This bias and uncertainty can be substantial enough to impede statistical convergence rates, even in seemingly straightforward analyses like mean calculation. The standard errors of such estimators then exhibit slower shrinkage with sample size than the typical 1 over root- n rate. This complicates fundamental calculations like p-values and confidence intervals, with no straightforward remedy currently available. In response to these challenges, we propose a new strategy that targets synthetic data created by DGMs for specific data analyses. Drawing insights from debiased and targeted machine learning, our approach accounts for biases, enhances convergence rates, and facilitates the calculation of estimators with easily approximated large sample variances. We exemplify our proposal through a simulation study on toy data and two case studies on real-world data, highlighting the importance of tailoring DGMs for targeted data analysis. This debiasing strategy contributes to advancing the reliability and applicability of synthetic data in statistical inference.
Downloads
-
preprint.pdf
- full text (Accepted manuscript)
- |
- open access
- |
- |
- 11.33 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JDF0G3R92V2S5NAE97CAAEF0
- MLA
- Decruyenaere, Alexander, et al. “Debiasing Synthetic Data Generated by Deep Generative Models.” Advances in Neural Information Processing Systems 37 (NeurIPS 2024), edited by A Globerson et al., vol. 37, 2024.
- APA
- Decruyenaere, A., Dehaene, H., Rabaey, P., Decruyenaere, J., Polet, C., Demeester, T., & Vansteelandt, S. (2024). Debiasing synthetic data generated by deep generative models. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), Advances in Neural Information Processing Systems 37 (NeurIPS 2024) (Vol. 37).
- Chicago author-date
- Decruyenaere, Alexander, Heidelinde Dehaene, Paloma Rabaey, Johan Decruyenaere, Christiaan Polet, Thomas Demeester, and Stijn Vansteelandt. 2024. “Debiasing Synthetic Data Generated by Deep Generative Models.” In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), edited by A Globerson, L Mackey, D Belgrave, A Fan, U Paquet, J Tomczak, and C Zhang. Vol. 37.
- Chicago author-date (all authors)
- Decruyenaere, Alexander, Heidelinde Dehaene, Paloma Rabaey, Johan Decruyenaere, Christiaan Polet, Thomas Demeester, and Stijn Vansteelandt. 2024. “Debiasing Synthetic Data Generated by Deep Generative Models.” In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), ed by. A Globerson, L Mackey, D Belgrave, A Fan, U Paquet, J Tomczak, and C Zhang. Vol. 37.
- Vancouver
- 1.Decruyenaere A, Dehaene H, Rabaey P, Decruyenaere J, Polet C, Demeester T, et al. Debiasing synthetic data generated by deep generative models. In: Globerson A, Mackey L, Belgrave D, Fan A, Paquet U, Tomczak J, et al., editors. Advances in Neural Information Processing Systems 37 (NeurIPS 2024). 2024.
- IEEE
- [1]A. Decruyenaere et al., “Debiasing synthetic data generated by deep generative models,” in Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, 2024, vol. 37.
@inproceedings{01JDF0G3R92V2S5NAE97CAAEF0,
abstract = {{While synthetic data hold great promise for privacy protection, their statistical analysis poses significant challenges that necessitate innovative solutions. The use of deep generative models (DGMs) for synthetic data generation is known to induce considerable bias and imprecision into synthetic data analyses, compromising their inferential utility as opposed to original data analyses. This bias and uncertainty can be substantial enough to impede statistical convergence rates, even in seemingly straightforward analyses like mean calculation. The standard errors of such estimators then exhibit slower shrinkage with sample size than the typical 1 over root-
n
rate. This complicates fundamental calculations like p-values and confidence intervals, with no straightforward remedy currently available. In response to these challenges, we propose a new strategy that targets synthetic data created by DGMs for specific data analyses. Drawing insights from debiased and targeted machine learning, our approach accounts for biases, enhances convergence rates, and facilitates the calculation of estimators with easily approximated large sample variances. We exemplify our proposal through a simulation study on toy data and two case studies on real-world data, highlighting the importance of tailoring DGMs for targeted data analysis. This debiasing strategy contributes to advancing the reliability and applicability of synthetic data in statistical inference.}},
author = {{Decruyenaere, Alexander and Dehaene, Heidelinde and Rabaey, Paloma and Decruyenaere, Johan and Polet, Christiaan and Demeester, Thomas and Vansteelandt, Stijn}},
booktitle = {{Advances in Neural Information Processing Systems 37 (NeurIPS 2024)}},
editor = {{Globerson, A and Mackey, L and Belgrave, D and Fan, A and Paquet, U and Tomczak, J and Zhang, C}},
issn = {{1049-5258}},
language = {{eng}},
location = {{Vancouver, Canada}},
pages = {{38}},
title = {{Debiasing synthetic data generated by deep generative models}},
url = {{https://papers.nips.cc/paper_files/paper/2024/hash/4902603fe8cb095b9ada707a19bd151c-Abstract-Conference.html}},
volume = {{37}},
year = {{2024}},
}