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

arXiv: https://arxiv.org/abs/2503.02093 · HTML: https://mlsanity.com/paper/2503.02093/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets](https://mlsanity.com/paper/2003.03685/) (2020)
- [A Survey on Causal Discovery Methods for I.I.D. and Time Series Data](https://mlsanity.com/paper/2303.15027/) (2023)
- [Sea Ice Forecasting using Attention-based Ensemble LSTM](https://mlsanity.com/paper/2108.00853/) (2021)
- [CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data](https://mlsanity.com/paper/2302.03246/) (2023)
- [Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity](https://mlsanity.com/paper/2311.00167/) (2023)
- [Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet](https://mlsanity.com/paper/2410.05638/) (2024)
- [SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting](https://mlsanity.com/paper/2410.14732/) (2024)

## Built on by (most cited first, 4 of 4)

- [TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery](https://mlsanity.com/paper/2506.01361/) (2025)
- [Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Prediction](https://mlsanity.com/paper/2509.09128/) (2025)
- [Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift](https://mlsanity.com/paper/2510.15265/) (2025)
- [Supraglacial Lake Fate Is Knowable Long Before the Season Ends](https://mlsanity.com/paper/2608.30113/) (2026)

## Abstract (arXiv)

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.