CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series
Yuxiao Cheng,
Lianglong Li,
Tingxiong Xiao
et al.
Abstract:Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. T… Show more
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