Castor: Causal Temporal Regime Structure Learning. (arXiv:2311.01412v1 [cs.LG])

The task of uncovering causal relationships among multivariate time series
data stands as an essential and challenging objective that cuts across a broad
array of disciplines ranging from climate science to healthcare. Such data
entails linear or non-linear relationships, and usually follow multiple a
priori unknown regimes. Existing causal discovery methods can infer summary
causal graphs from heterogeneous data with known regimes, but they fall short
in comprehensively learning both regimes and the corresponding causal graph. In
this paper, we …

array arxiv climate climate science data discovery healthcare linear multiple multivariate non-linear relationships science series temporal time series