arXiv Machine Learning

Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

arXiv:2510. 21889v2 Announce Type: replace-cross Abstract: Causal inference identifies cause-and-effect relationships between variables.

arXiv Machine Learning

Jun 17

SpatioTemporal Causal Network Diagnostics for Geographic Tipping Point Early Warning

arXiv:2606. 17553v1 Announce Type: new Abstract: Geographic tipping points in ecosystems, climate subsystems, or ice sheets pose severe challenges for localized early warning.

By Zhaoyuan Yu, Zhangyong Liang

benchmarks

arXiv Machine Learning

Jul 31

Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles

arXiv:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.

By S. A. Adedayo

benchmarks

arXiv Machine Learning

Aug 4

Physics constraints and response validation in discrete-time reduced-order modeling: from idealized turbulent systems to climate dynamics

arXiv:2602. 13847v5 Announce Type: replace-cross Abstract: A central challenge across science and engineering is to build data-driven reduced-order models of turbulent dynamical systems that reproduce stationary statistics, predict responses to external perturbations, and remain practical for real-world applications.

By Fabrizio Falasca, Laure Zanna

benchmarks

arXiv AI

Jul 22

Temporal-Causal Unity as an Operational Framework for Collective Dynamics: Causal-Progress Clocks, Synchronization, and Polarization

arXiv:2607. 18620v1 Announce Type: cross Abstract: This paper develops temporal-causal unity (TCU), a framework connecting a process-philosophical thesis -- time is the ordered unfolding of causal change -- to an operational model of cognitive and social dynamics.

By Jian Liu, Dong Sun

agents diffusion

arXiv Machine Learning

Aug 5

Amortized Interventional Forecasting for Multivariate CIR Processes

arXiv:2608. 03715v1 Announce Type: new Abstract: Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit default swap (CDS) spreads.

By Andreas Sauter, Sumit Sourabh, Drona Kandhai, Erman Acar

arXiv Machine Learning

Jun 4

Uncovering Insights of Compound Flooding with Data-Driven AI

arXiv:2506. 04281v2 Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention.

By Xu Zheng, Chaohao Lin, Sipeng Chen, Zhuomin Chen, Jimeng Shi, Jayantha Obeysekera, Jingchao Ni, Wei Cheng, Jason Liu, Dongsheng Luo

arXiv Machine Learning

Jul 15

Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty

arXiv:2508. 05659v4 Announce Type: replace Abstract: Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems.

By Jeroen F. Uleman, Loes Crielaard, Leonie K. Elsenburg, Guido A. Veldhuis, Naja Hulvej Rod, Rick Quax, V\'itor V. Vasconcelos

arXiv AI

Jun 10

WorldKernel: A World Model is the Coupling Kernel of Admissible Possible Worlds

arXiv:2606. 10934v1 Announce Type: new Abstract: A common assumption holds that enough observational and interventional data, given to a strong enough predictor, suffices.

By Fabio Rovai

arXiv AI

Jun 29

Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations

arXiv:2606. 27599v1 Announce Type: cross Abstract: While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent.

By Amadeo Tunyi

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv Machine Learning

Jul 31

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

arXiv:2607. 27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science.

By Dennis Thumm, Billy Tim Anthony, Ying Chen

benchmarks

arXiv Machine Learning

Aug 4

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

arXiv:2608. 01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference.

By Omar Faruque, Pavan Raj Ravi, Jianwu Wang

llms

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