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