AI RESEARCH
M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
arXiv CS.LG
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ArXi:2605.00398v1 Announce Type: new Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many grid cells than temporal observations per cell. The Causal Space-Time Stencil Learning (CaStLe) meta-algorithm was developed to address that niche under space-time locality and stationarity assumptions, but it is currently limited to univariate analyses. In this work, we present M-CaStLe.