Causal discovery in a complex industrial system: A time series benchmark

TL;DR

This paper introduces a new time series benchmark dataset with a known causal graph from an industrial system, enabling evaluation of causal discovery methods in complex real-world scenarios.

Contribution

It provides a unique dataset and causal graph from an industrial subsystem, facilitating the assessment and development of causal discovery algorithms for time series data.

Findings

Provides a real-world benchmark for causal discovery methods.

Enables comparison of causal inference accuracy against expert knowledge.

Supports advancement of causal analysis in complex industrial systems.

Abstract

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.