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

Emam Hossain, Muhammad Hasan Ferdous, Jianwu Wang, Aneesh Subramanian, Md Osman Gani
3/3/2025
cs.LGcs.AI

No official code found. Best implementation: ehfahad/Causal-DL-for-Arctic-SIE-Prediction (2 stars, GPL-3.0, last commit 2026-02-24).

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.

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Code Implementations(1)

ehfahad/Causal-DL-for-Arctic-SIE-PredictionCommunity60%
GPL-3.0

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

20Jupyter NotebookFeb 2, 20257 months agoGPL-3.0

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Cite this paper

@article{hossain2025correlation,
  title  = {Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction},
  author = {Emam Hossain and Muhammad Hasan Ferdous and Jianwu Wang and Aneesh Subramanian and Md Osman Gani},
  year   = {2025},
  eprint = {2503.02093},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.02093v1}
}

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