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Causal discovery from time series data is a typical problem setting acrossthe sciences. Often, multiple datasets of the same system variables areavailable, for instance, time series of river runoff from different catchments.The local catchment systems then share certain causal parents, such astime-dependent large-scale weather over all catchments, but differ in othercatchment-specific drivers, such as the altitude of the catchment. Thesedrivers can be called temporal and spatial contexts, respectively, and areoften partially unobserved. Pooling the datasets and considering the jointcausal graph among system, context, and certain auxiliary variables enables usto overcome such latent confounding of system variables. In this work, wepresent a non-parametric time series causal discovery method, J(oint)-PCMCI+,that efficiently learns such joint causal time series graphs when both observedand latent contexts are present, including time lags. We present asymptoticconsistency results and numerical experiments demonstrating the utility andlimitations of the method.

Causal discovery for time series from multiple datasets with latent contexts