arXiv Machine Learning
Credit Access is Associated with Improved Food Security in the Horn of Africa
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arXiv Machine Learning
Sep 22
Climate Variability Modulates the Impact of Price Spikes on Food Insecurity
arXiv:2609.24394v1 Announce Type: new Abstract: Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Ni\~no, tracked months befo...
arXiv Machine Learning
Jul 20
Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data
arXiv:2607. 15446v1 Announce Type: new Abstract: The cost of healthcare remains a concern in the United States and may have been influenced by disruptions associated with the COVID-19 pandemic.
Hugging Face Trending Papers
Sep 14
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...
arXiv AI
Sep 15
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
The paper presents a transfer‑learning approach that adapts a multimodal spatiotemporal vision transformer, originally trained on Demographic and Health Survey data, to estimate socioeconomic conditions in forced‑displacement settings. Using satellite‑derived geospatial covariates, the adapted model explains up to 66% of variation in socioeconomic outcomes in camp‑intersecting grids and 41% in non‑camp areas, achieving mean absolute errors of 4.37 and 5.41 index points respectively. This framework supplements periodic household surveys by providing regularly updated, spatially granular socioeconomic estimates that bridge data gaps between survey rounds.
arXiv Machine Learning
Aug 28
District-Level Food Environment Indicators and Social Vulnerability in S\~ao Paulo
This study examined whether food retail and street‑market indicators differentiate social vulnerability levels across 76 districts of São Paulo. Using machine‑learning classifiers on district‑level data, the authors found that densities of healthy and unhealthy food establishments explained about 60 % of feature importance, with XGBoost achieving the highest mean F‑score (0.75). The results suggest that publicly available food‑environment data are linked to district‑level social vulnerability, though limitations such as small sample size and cross‑sectional design restrict causal inference.
arXiv Machine Learning
Jun 16
From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification
arXiv:2606. 17010v1 Announce Type: new Abstract: Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly.