Technology Category
Machine Learning: Causal LearningReasoning under Uncertainty: CausalityKnowledge Representation and Reasoning: Action, Change, and Causality
Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction
🤖 AI Summary
Traditional machine learning and deep learning predominantly rely on correlational modeling, failing to distinguish genuine causal relationships from spurious associations—thereby limiting model robustness, interpretability, and generalizability. To address this challenge in Arctic sea ice extent forecasting, we propose a causally-aware deep learning framework. First, we integrate multivariate Granger causality (MVGC) with the PCMCI+ algorithm for causal feature selection, identifying climate variables exerting direct causal effects on sea ice dynamics. Second, we embed a causal-driven input screening mechanism into a hybrid neural network architecture to achieve feature compression and enhanced interpretability. Evaluated on 43 years of observational data, our method achieves statistically significant improvements in multi-step prediction accuracy, while simultaneously boosting generalization capability and computational efficiency. This work establishes a novel paradigm for climate prediction that harmonizes causal plausibility with practical performance.
Technology Category
Application Category
📝 Abstract
Conventional machine learning and deep learning models typically rely on correlation-based learning, which often fails to distinguish genuine causal relationships from spurious associations, limiting their robustness, interpretability, and ability to generalize. To overcome these limitations, we introduce a causality-aware deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ for causal feature selection within a hybrid neural architecture. Leveraging 43 years (1979-2021) of Arctic Sea Ice Extent (SIE) data and associated ocean-atmospheric variables at daily and monthly resolutions, the proposed method identifies causally influential predictors, prioritizes direct causes of SIE dynamics, reduces unnecessary features, and enhances computational efficiency. Experimental results show that incorporating causal inputs leads to improved prediction accuracy and interpretability across varying lead times. While demonstrated on Arctic SIE forecasting, the framework is broadly applicable to other dynamic, high-dimensional domains, offering a scalable approach that advances both the theoretical foundations and practical performance of causality-informed predictive modeling.
Problem
Research questions and friction points this paper is trying to address.
Causality-aware modeling for Arctic sea ice prediction
Identifying causally influential predictors from spurious correlations
Enhancing prediction accuracy and interpretability across lead times
Innovation
Methods, ideas, or system contributions that make the work stand out.
Causal feature selection via MVGC and PCMCI+
Hybrid neural architecture integrating causality
Leveraging 43 years of Arctic climate data
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