DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

arXiv:2602.01433v2 Announce Type: replace-cross Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and autocorrelation. Existing causal discovery methods typically operate on raw observations, making them vulnerable to spurious edges and misattributed temporal dependencies. We introduce a decomposition-based causal discov