Causal networks for risk and compliance: Methodology and application

Abstract

This paper presents a statistical approach to quantitatively measure the current exposure of a company to failures and defects in product quality or to compliance to government regulations. This approach is based on causal networks, which have previously been applied to other fields, such as systems maintenance and reliability. Causal networks allow analysts to causally explain the values of variables (an explanatory approach), to assess the effect of interventions on the structure of the data-generating process, and to evaluate Bwhat-if[ scenarios, that is, alternative methods or policies (an exploratory approach). Building the causal structure raises some challenges. In particular, there is no automated way to collect the needed data. We present a methodology for model selection and probability elicitation based on expert knowledge. We apply the proposed approach to the case of pharmaceutical manufacturing processes. The use of such networks allows for a more rigorous comparison of practices across different manufacturing sites, creates the opportunity for risk remediation, and allows us to evaluate alternative methods and approaches. © 2010 IBM.