CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
It provides datasets and benchmarks for researchers and practitioners in industrial process control to compare causal discovery methods, though it is incremental as it focuses on new data rather than novel methods.
This work introduces two novel public datasets for benchmarking causal discovery methods in continuous manufacturing processes, using the Tennessee Eastman simulator and an ultra-processed food plant, and proposes a benchmarking procedure evaluated on various algorithms to enable method selection for specific applications.
Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal Discovery (CD). Considering the growing number of recently proposed CD methods, it is necessary to introduce strict benchmarking procedures on publicly available datasets since they represent the foundation for a fair comparison and validation of different methods. This work introduces two novel public datasets for CD in continuous manufacturing processes. The first dataset employs the well-known Tennessee Eastman simulator for fault detection and process control. The second dataset is extracted from an ultra-processed food manufacturing plant, and it includes a description of the plant, as well as multiple ground truths. These datasets are used to propose a benchmarking procedure based on different metrics and evaluated on a wide selection of CD algorithms. This work allows testing CD methods in realistic conditions enabling the selection of the most suitable method for specific target applications. The datasets are available at the following link: https://github.com/giovanniMen