arXiv Machine Learning June 16, 2026 By Yang Han, Jacqueline CK Lam, Victor OK Li, Yiu-Wai Man

AI for Social Good: An Investigation of the Causal Relationship Between Environmental Regulations and Their Effects on Air Pollution in London, UK

Read the original on arXiv Machine Learning →

arXiv:2606. 15257v1 Announce Type: new Abstract: Air pollution regulation is central to urban public health governance, but estimating its effects is difficult because policies are implemented non-randomly and pollution trajectories are shaped by meteorology, socioeconomic change, temporal trends, and overlapping interventions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 5

Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia

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.

By Jonathan Colen, Eric Werner, Maryam Golbazi, Heather Richter, Diana McSpadden, Amy Quinn, Jocel Santos, Mary Jane Darling, Mary Margaret Gleason

safety

arXiv Machine Learning

Jul 21

Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

arXiv:2607. 17024v1 Announce Type: new Abstract: Air pollution and climate-related stressors are increasingly important concerns for respiratory health, especially in settings with unequal environmental exposure and healthcare capacity.

By Maede Azani Hassan Abadi, Shouyi Wang

arXiv AI

Aug 21

Causal Inference under Interference with Learned Exposure Mappings

arXiv:2608. 19224v1 Announce Type: cross Abstract: Exposure mappings are often assumed to be known in causal spillover analyses.

By Cong Cao

arXiv Machine Learning

5d ago

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

The paper presents a hierarchical causal representation learning framework that models both internal climate variability and forced responses in sea surface temperature fields from a global climate model. By training on future climate change scenarios, the method accurately predicts long‑term global mean and regional temperature evolution and reproduces realistic responses to perturbations in greenhouse gas and aerosol concentrations on unseen scenarios. This demonstrates the potential of causal representation learning to improve climate model emulation.

By Shan Zhao, Ilija Trajkovic, Julia Kaltenborn, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard

benchmarks

arXiv Machine Learning

Jul 23

Air Quality Arena: A Large-Scale Multi-Region Ground Monitoring Dataset and Benchmark for Air Quality Forecasting with Time-Series Foundation Models

arXiv:2607. 19381v1 Announce Type: new Abstract: Air pollution causes an estimated 7.

By Rishi Bharadwaj, Manik Gupta, Pandarasamy Arjunan

multimodal benchmarks

arXiv Machine Learning

Jul 15

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents

arXiv:2511. 23276v2 Announce Type: replace Abstract: Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports.

By Joongwon Chae, Runming Wang, Chen Xiong, Gong Yunhan, Lian Zhang, Ji Jiansong, Dongmei Yu, Peiwu Qin

llms agents

One email a morning, machine-written

Related stories

Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia

Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

Causal Inference under Interference with Learned Exposure Mappings

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

Air Quality Arena: A Large-Scale Multi-Region Ground Monitoring Dataset and Benchmark for Air Quality Forecasting with Time-Series Foundation Models

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents