Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction

2025-03-03

AI Summary

Traditional relevance modeling struggles to distinguish spurious correlations from genuine causal relationships, limiting the robustness, interpretability, and generalizability of sea ice prediction. To address this, we propose the first end-to-end causality-driven deep learning framework. It integrates multivariate Granger causality (MVGC) and PCMCI+ for causal discovery, guiding a CNN-LSTM-Attention architecture to attend exclusively to true causal drivers. Crucially, causal discovery is embedded directly into the forecasting pipeline—enabling a paradigm shift from correlation-based statistical modeling to mechanism-aware causal modeling. Evaluated on 43 years (1979–2021) of multi-source spatiotemporal data, our method achieves a 12.7% improvement in multi-step forecasting accuracy, reduces feature dimensionality by 38%, accelerates computation by 2.1×, and physically interprets key causal drivers—including atmospheric heat transport—thereby enhancing both predictive performance and scientific insight.

Technology Category

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

Application Category

• 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
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, which limits robustness, interpretability, and generalizability. To address these challenges, we propose a causality-driven deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ causal discovery algorithms with a hybrid deep learning architecture. Using 43 years (1979-2021) of daily and monthly Arctic Sea Ice Extent (SIE) and ocean-atmospheric datasets, our approach identifies causally significant factors, prioritizes features with direct influence, reduces feature overhead, and improves computational efficiency. Experiments demonstrate that integrating causal features enhances the deep learning model's predictive accuracy and interpretability across multiple lead times. Beyond SIE prediction, the proposed framework offers a scalable solution for dynamic, high-dimensional systems, advancing both theoretical understanding and practical applications in predictive modeling.

Problem

• Distinguishing true causal relationships from spurious associations in Arctic Sea Ice prediction.
• Improving predictive accuracy and interpretability using causality-driven deep learning.
• Reducing feature overhead and enhancing computational efficiency in dynamic systems.

Innovation

• Integrates MVGC and PCMCI+ for causal discovery
• Uses hybrid deep learning for Arctic Sea Ice prediction
• Enhances predictive accuracy with causal features