The use of synthetic data to deidentify data and to improve predictive models is well-attested to. The augmentation of datasets using synthetically generated data is an alluring proposition: in the best case, it generates realistic data \textit{in silico} at a fraction of the cost of authentic data which may be found \textit{in vivo} or \textit{in vitro}. This poses novel epistemic challenges. We contend that synthetic data augmentation is best understood as a novel way of accounting for prior knowledge. In this manuscript, we propose a definition of synthetic distributions and analyze how synthetic data augmentation interplays with standard accounts of maximum likelihood and Bayesian estimation. We observe that the marginal Fisher information contributed by synthetic data processes is subject to fundamental bounds, and enumerate obstacles to the use of synthetic data augmentation to aid in inferential tasks. We then articulate a Bayesian formulation of the way that synthetic data augmentation can be coherently understood, but argue that naive approaches to the specification of the prior are epistemically unjustifiable. This suggests that enhanced scrutiny must be placed on identifying justifiable priors to warrant the use and inclusion of data drawn from specific synthetic distributions. While our analysis shows the challenges and limitations of using synthetic data augmentation to improve upon traditional statistical model reasoning, it does suggest that augmentation is the principal approach analysts using outcome reasoning (i.e. using train/test splits to justify the analysis) can constrain an otherwise high-dimensional model space, providing an alternative to trying to encode the constraints into the potentially complex architecture of the algorithm.

翻译：使用合成数据进行数据脱敏和提升预测模型的有效性已得到充分证实。通过合成生成的数据集进行扩增是一种极具吸引力的方案：在最佳情况下，它能以极低成本生成与真实数据（如体内或体外数据）相似的计算机模拟数据。这带来了新的认识论挑战。我们认为，合成数据扩增应被理解为一种整合先验知识的新方法。本文提出了合成分布的定义，并分析了合成数据扩增如何与标准极大似然估计和贝叶斯估计相互作用。我们观察到，合成数据过程提供的边际Fisher信息受制于基本界限，并列举了利用合成数据扩增辅助推断任务所面临的障碍。随后，我们阐述了合成数据扩增可被连贯理解的贝叶斯公式，但指出先验设定的朴素方法在认识论上缺乏合理性。这表明必须加强对可辩护先验的审查，以证明使用特定合成分布生成的数据的合理性。尽管我们的分析揭示了使用合成数据扩增改进传统统计模型推理的挑战与限制，但结果表明，对于采用结果推理（即通过训练/测试分割验证分析）的分析者而言，扩增是约束高维模型空间的主要手段，为将约束编码至算法可能复杂的架构提供了替代方案。