Paper Proposes Confounder-Aware Multi-View Learning for Urban Region Embeddings

A new arXiv paper argues that standard urban region representation learning, which merges data such as mobility flows, points of interest and land-use, can be misled by confounding factors that create spurious correlations. The authors introduce a confounder-aware multi-view approach intended to improve downstream tasks like mobility analysis, public safety forecasting and service demand estimation. The work appears in the cs.AI and cs.LG listings.