Author: Andres Alfredo Ruiz-Tagle Palazuelos
Title: Exploiting Causal Reasoning to Improve the Quantitative Risk Assessment of Engineering Systems Through Interventions and Counterfactuals
Date and Time: 04/10/2023 at 11:30 AM
Location: Clark Memorial Conference Room 1117 in A. James Clark Hall
Zoom invite: https://umd.zoom.us/j/2803971669?pwd=SXZPazNZamczZ0xrMEVxV3MxTC9sQT09
Committee members:
- Dr. Katrina Groth, Chair/Advisor
- Dr. Enrique Lopez Droguett
- Dr. Michelle Bensi
- Dr. Jeffrey Herrmann
- Dr. Michel Cukier
- Dr. Gregory Baecher, Dean’s Representative
Abstract:
The main strength of quantitative risk assessment (QRA) is to enable risk management by providing causal insights into the risk of an engineering system or process. Bayesian Networks (BNs) have become popular in QRA because they offer an improved causal structure that represents analysts’ knowledge of a system and enable reasoning under uncertainty. Currently, the use of BNs for risk-informed decisions is based solely on associative reasoning, answering questions of the form “If we observe X=x, how likely is it to observe Y=y?” However, risk management in the industry relies on understanding how a system could change in response to external influences (e.g., interventions and decisions) and identifying the causes and mechanisms that could explain the outcome of past events (e.g., accident investigations and lessons learned). This dissertation shows that associative reasoning alone is insufficient to provide these insights, and it provides a framework for obtaining more complex causal insight using BNs with intervention and counterfactual reasoning.
Intervention and counterfactual reasoning must be implemented along with BNs to provide more complex insights about the risk of a system. Intervention reasoning answers queries of the form “How does doing X=x change the likelihood of observing Y=y?” and can be used to inform the causal effect of interventions and decisions on the risk and reliability of a system. Counterfactual reasoning answers queries of the form “Had X been X=x’ in an event, instead of the observed X=x, could Y have been Y=y’, instead of the observed Y=y?” and can be used to learn from past events and improve safety management activities. BNs present a unique opportunity as a risk modeling approach that incorporates the complex causal dependencies present in a system’s variables and allows reasoning under uncertainty. Therefore, exploiting the causal reasoning capabilities of BNs in QRAs can be highly beneficial to improve modern risk analysis.
The goal of this work is to define how to exploit the causal reasoning capabilities of BNs to support intervention and counterfactual reasoning in the QRA of complex systems and processes. To achieve this goal, this research first establishes the mathematical background and methods required to model interventions and counterfactuals within a BN approach. Then, we demonstrate the proposed methods with two case studies concerning the risk of third-party excavation damage to natural gas pipelines in the U.S. The first case study showed that the intervention reasoning methods developed in this work produce unbiased causal insights into the effectiveness of implementing new excavation practices. The second case study showed how the counterfactual reasoning methods developed in this work can expand on the lessons learned from an accident investigation on the Sun Prairie 2018 gas explosion by providing new insights into the effectiveness of current damage prevention practices. Finally, associative, intervention, and counterfactual reasoning methods with BNs were integrated into a single model and used to assess the risk of a highly complex challenge for the future of clean energy: excavation damages to natural gas pipelines transporting hydrogen. The impact of this research is a first-of-its-kind approach and a novel set of QRA methods that provide expanded causal insights for understanding failures and accidents in complex engineering systems and processes.