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: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.
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...
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.
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: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.
The paper presents an uncertainty‑aware machine‑learning approach for mapping poverty in Africa using satellite imagery. By combining simultaneous quantile regression with a novel conformal prediction technique, the authors generate statistically guaranteed prediction intervals for neighborhood‑level International Wealth Index estimates, achieving high explanatory power (R² = 0.75) while acknowledging broader uncertainty. They also propose a risk‑controlled aid allocation procedure that leverages both survey data and model predictions, showing in simulations that it can deliver more aid per eligible recipient than alternative strategies.
Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics.
The paper proposes Causal Evidentiary Governance (CEG), a framework that requires regulated institutions to maintain a versioned directed acyclic graph (DAG) separating allowable from disallowed causal pathways in high‑risk machine learning systems. CEG introduces the Causal Harm Rate to quantify prediction variation due to disallowed pathways and pairs each decision with a signed Decision‑Evidence Packet (DEP) that cryptographically links the prediction to the DAG and path‑specific attributions, enabling efficient inclusion proofs via a Merkle tree. Empirical validation on synthetic credit data and the German Credit dataset demonstrates that CEG more clearly isolates causal effects than traditional fairness metrics and that a proof‑of‑concept implementation shows operational feasibility with manageable performance tradeoffs.
The paper introduces DeepX-GAN, a deep generative model that captures spatial dependence in rare climate extremes. It can simulate statistically plausible unseen heat extremes—both direct-hit and near-miss events—beyond the observed record. Applied to the Middle East and North Africa, the model shows that unseen heat extremes disproportionately affect vulnerable countries and that future warming could create new persistent hotspots, underscoring the need for spatially adaptive resilience planning.
arXiv:2606. 05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes.
arXiv:2606. 06174v1 Announce Type: new Abstract: Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors.