Causal discovery in a complex industrial system: A time series benchmark
Søren Wengel Mogensen Karin Rathsman Per Nilsson
Causal discovery outputs a causal structure, represented by a graph, from
observed data. For time series data, there is a variety of methods, however, it
is difficult to evaluate these on real data as realistic use cases very rarely
come with a known causal graph to which output can be compared. In this paper,
we present a dataset from an industrial subsystem at the European Spallation
Source along with its causal graph which has been constructed from expert
knowledge. This provides a testbed for causal discovery from time series
observations of complex systems, and we believe this can help inform the
development of causal discovery methodology.