Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction

2025-09-10

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

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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

• Causality-aware modeling for Arctic sea ice prediction
• Identifying causally influential predictors from spurious correlations
• Enhancing prediction accuracy and interpretability across lead times

Innovation

• Causal feature selection via MVGC and PCMCI+
• Hybrid neural architecture integrating causality
• Leveraging 43 years of Arctic climate data