Debiasing Synthetic Data Generated by Deep Generative Models

2024-11-06

arXiv.org

AI Summary

Synthetic data generated by deep generative models (DGMs) often induce statistical inference bias and degrade convergence rates—e.g., distorted p-values and under-coverage of confidence intervals. To address this, we introduce, for the first time, a task-oriented double-debiasing framework for synthetic data analysis, integrating debiased learning and targeted machine learning principles. Our method jointly optimizes DGM latent-space fine-tuning and sample reweighting using influence functions and targeted maximum likelihood estimation. Theoretically, it restores √n-consistency for key estimands; empirically, it significantly improves standard error estimation accuracy for statistics such as the mean in simulations and two real-world case studies, yielding well-calibrated p-values and confidence intervals with valid frequentist properties and interpretability. Our core contribution is the first task-specific synthetic data debiasing paradigm that simultaneously provides rigorous theoretical guarantees and strong empirical robustness.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Deep Generative Models
• Synthetic Data
• Statistical Analysis Error

Innovation

• Debiased Targeted Learning
• Customized Deep Generative Models
• Enhanced Statistical Analysis