Comparative Evaluation of Causal Discovery and Inference Approaches on Arctic Sea Ice Time Series Data

Sea ice extent plays a crucial role in the Arctic system, and thus the study on causal relationships between sea ice extent and other climate variables comes to our interest to better understand the system. To find the causal relationship we applied various state-of-the-art causal discovery techniques from the time-independent and time-dependent domains. Then we employed several causal inference models to quantify the causal effects of different causal relationships in the Arctic system. The NSIDC Sea Ice Concentration observation data and the ERA-5 global reanalysis data were used in our study. Our analysis shows that the GES and VarLiNGAM from causal discovery methods and the conditional instrumental variable (CIV) causal inference model perform better on the Arctic Sea Ice time series dataset.