合成数据在科研中越来越常见，但偏差问题一直让人头疼。这篇论文给出了一个可操作的统计框架，让做社会科学调查或AI评估的研究者可以放心地用合成数据做推断，值得关注。
该论文提出了一种名为“任务可交换性”的统计条件，允许研究人员在合成数据存在偏差和噪声的情况下，仍能进行具有可证明有效性的推断。核心思想是：如果当前研究任务与某些已有真实数据的“历史任务”在数学上可交换，那么就可以利用合成数据来扩展研究，同时保证统计结论的可靠性。作者在公众舆论调查（使用“硅样本”）和AI评估（使用自动评分器）两个场景中验证了该框架。这项工作为社会科学、AI评测等领域安全使用合成数据提供了理论基础。
Valid Inference with Synthetic Data via Task Exchangeability
There is a proliferation of work arguing for the use of synthetic data in scientific research. For example, social scientists are arguing for the use of LLM-generated "silicon samples" in pilot studies; AI evaluations increasingly rely on "LLM-as-a-judge" outputs; and proteomics research is accelerated by generative models that produce synthetic protein structures. These developments raise an intriguing possibility: synthetic data may help researchers ask more questions, run more studies, and accelerate discovery. But they also raise a fundamental concern: synthetic data can be biased, noisy, and misspecified. In this work, we propose statistical principles for using synthetic data in scientific research with provable validity guarantees. The key insight is a new technical condition that we call task exchangeability. Informally, this is a requirement that the researcher can identify historical tasks, for which real data is available, such that their current task of interest is exchangeable with the historical tasks in an appropriate mathematical sense. We develop methods for valid inference under task exchangeability, together with extensions that provide guarantees even beyond exchangeability. We demonstrate the framework on public opinion surveys with silicon samples and AI evaluation with autoraters.