Learning a Size-Weight Frontier for Synthetic-Augmented Inference
Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference. We develop a general framework for synthetic-augmented inference across a population of related tasks. It characterizes synthetic augmentation by the number of synthetic observations and their weight. Central to our framework is a size-weight frontier that specifies, for each weight, the largest synthetic sample size for which all smaller sizes attain the target task-marginal coverage. We estimate this frontier from historical tasks, and establish a finite-sample coverage guarantee simultaneously for all size-w...
| Authors | Chengpiao Huang, Kaizheng Wang |
|---|---|
| Primary category | stat.ME |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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