Data scarcity limits inference in many scientific and policy domains. Survey data are essential for decision-making, but sparse samples often fail to capture fine spatial granularities. We evaluate normalizing flows, a generative model that learns complex data distributions and can be conditioned on exogenous contextual features, in controlled data scarcity scenarios. Across eight household survey datasets spanning six low-income or middle-income countries in the humanitarian domain, we show that context-conditioned generative models can refine sub-national survey distributions under severe data scarcity, and that performance increases systematically with the richness of the conditioning information. These findings support a general principle for survey data augmentation: generative models can improve sub-national estimates when the sparse sample retains sufficient support and contextual covariates encode relevant local heterogeneity. By learning full conditional distributions rather than point estimates, the approach provides fine-grained evidence for humanitarian decision-making and resource allocation.

翻译：数据稀缺限制了众多科学和政策领域的推断。调查数据对决策至关重要，但稀疏样本往往无法捕捉精细的空间粒度。我们评估了正则化流（一种能够学习复杂数据分布并可基于外生上下文特征进行条件化的生成模型）在受控数据稀缺场景中的性能。基于涵盖人道主义领域六个低收入或中等收入国家的八组家庭调查数据集，我们证明：上下文条件生成模型能在严重数据稀缺条件下优化子国家调查分布，且性能随条件信息丰富度的提升而系统性增强。这些发现支持调查数据增强的一般原则：当稀疏样本保留足够支撑且上下文协变量编码相关局部异质性时，生成模型可改善子国家估计。通过学习完整条件分布而非点估计，该方法为人道主义决策和资源分配提供了细粒度证据。