arXiv Machine Learning

Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports

The paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that estimates latent causes from observed effects by exploiting both task-invariant and task-specific structural dependencies. MTAC constructs a structural equation model that separates a shared backbone mechanism from task-specific deviations, then uses maximum a posteriori inference to reconstruct causes. Applied to urban event reconstruction—parking violations, abandoned properties, and unsanitary conditions—MTAC outperforms strong baselines on real data from Manhattan and Newark, achieving up to a 33.04% reduction in mean absolute error.

arXiv Machine Learning

Jul 24

Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility

arXiv:2607. 21444v1 Announce Type: cross Abstract: Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services.

By Cande Lian (School of Management, Foshan University, Foshan, China), Wentao Zeng (School of Management, Foshan University, Foshan, China), Jiabin Wu (School of Management, Foshan University, Foshan, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Wei Zhou (Department of Civil and Environmental Engineering, National University of Singapore)

safety

arXiv Machine Learning

Aug 19

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.

By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye

llms benchmarks

arXiv Machine Learning

Sep 22

OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling

arXiv:2609.23753v1 Announce Type: cross Abstract: Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeli...

By Yikun Miao, Fangqi Zhu, Quanxin Shou, Xiaoyi Pang, Zhengyang Yan, Junhao Li, Haodong Wang, Zicong Hong, Song Guo

fine-tuning

arXiv AI

2d ago

Structure-agnostic Causal Representation Learning

arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requi...

By Arman Behnam, Binghui Wang

benchmarks

Hugging Face Trending Papers

Jul 23

Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility

Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services. Shifting operation from reactive repair to preventive maintenance depends on accurate, forward-looking fault-risk prediction, a task complicated by the heterogeneous time scales of physical, behavioral, contextual, and historical signals and by forecasting over a multi-week horizon.

safety

arXiv Machine Learning

Jun 5

CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

arXiv:2606. 05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making.

By Zhaoqi Zhang, Miao Xie, Yi Li, Linyou Cai, Siqiang Luo, Gao Cong

benchmarks

arXiv Machine Learning

Aug 24

Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification

arXiv:2608.20548v1 Announce Type: cross Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplore...

By Fuad Hasan, Chul Min Yeum

arXiv Machine Learning

Aug 11

CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

arXiv:2608. 08064v1 Announce Type: new Abstract: Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes.

By Gerrit Gro{\ss}mann, Sumantrak Mukherjee, Sebastian J. Vollmer

arXiv AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

arXiv AI

Jul 28

EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation

arXiv:2607. 22655v1 Announce Type: new Abstract: Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience.

By Jie Zhao, Jie Feng, Can Rong, Zhihan Hou, Peng Lu, Yong Li

llms rag diffusion

arXiv Machine Learning

Jul 14

Causal Foundation Models with Continuous Treatments

arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.

By Christopher Stith, Medha Barath, Vahid Balazadeh, Jesse C. Cresswell, Rahul G. Krishnan

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 14

Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture

arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.

By Wentao Zeng (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China, School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China), Zijian Huang (School of Artificial Intelligence, South China Normal University, Guangzhou, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Jiabin Wu (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China), Jun Gong (Department of Civil Engineering, The University of Hong Kong, Hong Kong, China)

benchmarks

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