Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics

ArXi:2601.17647v2 Announce Type: replace-cross Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms driving polar climate change and global sea-level rise. Conventional deep learning models often struggle with treatment effect estimation in climate settings due to time-varying confounding and the lack of physical constraints. To address these challenges, we propose the Knowledge-Guided Causal Model Variational Autoencoder (KGCM-VAE) to quantify the effect of SSH on sea ice thickness.