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Harnessing Synthetic Data from Generative AI for Statistical Inference

Ahmad Abdel-Azim, Ruoyu Wang, Xihong Lin

TL;DR

This paper reviews the current landscape of synthetic data generation and use from a statistical perspective, with the goal of clarifying the assumptions under which synthetic data can meaningfully support downstream discovery, inference, and prediction.

Abstract

The emergence of generative AI models has dramatically expanded the availability and use of synthetic data across scientific, industrial, and policy domains. While these developments open new possibilities for data analysis, they also raise fundamental statistical questions about when synthetic data can be used in a valid, reliable, and principled manner. This paper reviews the current landscape of synthetic data generation and use from a statistical perspective, with the goal of clarifying the assumptions under which synthetic data can meaningfully support downstream discovery, inference, and prediction. We survey major classes of modern generative models, their intended use cases, and the benefits they offer, while also highlighting their limitations and characteristic failure modes. We additionally examine common pitfalls that arise when synthetic data are treated as surrogates for real observations, including biases from model misspecification, attenuated uncertainty, and difficulties in generalization. Building on these insights, we discuss emerging frameworks for the principled use of synthetic data. We conclude with practical recommendations, open problems, and cautions intended to guide both method developers and applied researchers.

Harnessing Synthetic Data from Generative AI for Statistical Inference

TL;DR

This paper reviews the current landscape of synthetic data generation and use from a statistical perspective, with the goal of clarifying the assumptions under which synthetic data can meaningfully support downstream discovery, inference, and prediction.

Abstract

The emergence of generative AI models has dramatically expanded the availability and use of synthetic data across scientific, industrial, and policy domains. While these developments open new possibilities for data analysis, they also raise fundamental statistical questions about when synthetic data can be used in a valid, reliable, and principled manner. This paper reviews the current landscape of synthetic data generation and use from a statistical perspective, with the goal of clarifying the assumptions under which synthetic data can meaningfully support downstream discovery, inference, and prediction. We survey major classes of modern generative models, their intended use cases, and the benefits they offer, while also highlighting their limitations and characteristic failure modes. We additionally examine common pitfalls that arise when synthetic data are treated as surrogates for real observations, including biases from model misspecification, attenuated uncertainty, and difficulties in generalization. Building on these insights, we discuss emerging frameworks for the principled use of synthetic data. We conclude with practical recommendations, open problems, and cautions intended to guide both method developers and applied researchers.
Paper Structure (18 sections, 19 equations, 2 figures, 3 tables)

This paper contains 18 sections, 19 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Synthetic data generation
  3. Why synthetic data?
  4. Privacy-preserving release
  5. Data augmentation
  6. Fairness.
  7. Domain transfer
  8. Missing data/trajectory completion
  9. Generative models for synthetic data
  10. Generative model choice.
  11. Modern extensions
  12. The use of synthetic data in downstream analysis
  13. The use of synthetic data: model misspecification, uncertainty, efficiency, and generalization ability
  14. Synthetic data-based approaches
  15. Synthetic data-assisted approaches
  16. ...and 3 more sections

Figures (2)

  • Figure 1: An illustration of the difference between synthetic data-based and synthetic data-assisted approaches in the semisupervised regression context using AutoComplete an2023deep and SynSurr mccaw2024synthetic as examples. AutoComplete pools the synthetic data and real data to conduct inference, while SynSurr uses synthetic data to construct $\hat{e}$ to assist the inference based on labeled data.
  • Figure 2: An illustration of RICE wang2022out -- a regularization-based synthetic data-augmented method. To build a model to robustly classify images of diverse styles, RICE generates synthetic images of different styles (e.g., cartoon and photo) based on each real image (e.g., painting). The RICE training procedure impose regularization terms to encourage the model to perform similarly on real images and their synthetic counterparts.