Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys
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arXiv
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| Format: | Preprint |
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2026
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| author | Sibilla, Federica Voukelatou, Vasiliki Piovani, Duccio Koupparis, Kyriacos Paolotti, Daniela Schifanella, Rossano Kalimeri, Kyriaki |
| author_facet | Sibilla, Federica Voukelatou, Vasiliki Piovani, Duccio Koupparis, Kyriacos Paolotti, Daniela Schifanella, Rossano Kalimeri, Kyriaki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31489 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys Sibilla, Federica Voukelatou, Vasiliki Piovani, Duccio Koupparis, Kyriacos Paolotti, Daniela Schifanella, Rossano Kalimeri, Kyriaki Computers and Society 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. |
| title | Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2605.31489 |