Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction
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(Published version)
Date
2025
Authors
Gao, W.
Li, J.
Cheng, D.
Liu, L.
Liu, J.
Le, T.
Du, X.
Chen, X.
Chen, Y.
Zhao, Y.
Editors
Kwok, J.
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Conference paper
Citation
IJCAI International Joint Conference on Artificial Intelligence, 2025 / Kwok, J. (ed./s), pp.9638-9646
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Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25} (16 Aug 2025 - 22 Aug 2025 : Montreal, Canada)
Abstract
<jats:p>Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias correction methods, which rely on historical observation data and statistical techniques, often neglect unobserved confounders, leading to biased results. This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders. Inspired by recent advances in causality based time series deconfounding, our method first constructs a factor model to learn latent confounders from historical data and then applies them to enhance the bias correction process using advanced time series forecasting models. The experimental results demonstrate significant improvements in the accuracy of precipitation outputs. By addressing unobserved confounders, our approach offers a robust and theoretically grounded solution for climate model bias correction.</jats:p>
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Copyright 2025 The author(s).