When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning

• URL: http://arxiv.org/abs/2609.15305v1
• Date: Mon, 14 Sep 2026 09:53:56 GMT
• Title: When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
• Abstract summary: We propose CURE, a confounder-aware framework for multi-view urban region representation learning.<n>CURE encodes each view with its regional graph structure, estimates a shared latent component, and then reduces its projected influence before cross-view interaction.<n> Experiments on three real-world cities show that CURE improves predictive performance, remains robust under missing and noisy input views, and provides reliable cross-view integration through shared component separation and context-dependent view weighting.
• Score: 16.301090791658545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including mobility analysis, public safety forecasting, and service demand estimation. Existing multi-view methods typically improve region embeddings by strengthening interactions across views. However, such methods often overlook view-specific regional structures and may propagate correlations induced by shared latent factors, which can reduce the stability of downstream predictions. To overcome this major limitation, we propose CURE, a confounder-aware framework for multi-view urban region representation learning. CURE first encodes each view with its regional graph structure, estimates a shared latent component, and then reduces its projected influence before cross-view interaction. A hierarchical graph-aware fusion module subsequently aggregates the residual view representations using local and global regional contexts Experiments on three real-world cities show that CURE improves predictive performance, remains robust under missing and noisy input views, and provides reliable cross-view integration through shared component separation and context-dependent view weighting.

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