A data driven approach to elicit causal links between performance shaping factors and human failure events

Within the field of human reliability analysis (HRA), there is an acknowledged demand to move further towards data driven models. There have been several independent research projects focused on gathering the required empirical data, to support existing theoretical models used in HRA, as well as allow the use of probabilistic tools, such as Bayesian Networks, to model such data. However, with regards to Bayesian Networks, there is a reliance upon expert elicitation to design the structure of the network, that is, the causal links between the considered factors are determined by an expert, with the data used only to estimate the conditional probability tables. This work aims to provide a methodology/framework to elicit causal links between performance shaping factors from data, producing a HRA model constructed entirely from data, with the ability to integrate the knowledge provided by experts. The Multi-Attribute Technological Accidents Dataset (MATA-D) has been used as the data source, therefore the model is produced under a framework based on the Cognitive Reliability and Error Analysis Method (CREAM). This model is produced through a combination of information theory and structure learning algorithms for Bayesian Networks. The proposed model/methodology aims to support experts in their evaluation of human error probability, and reveal causal links between performance shaping factors, that may not have otherwise been considered.