Causal Graphical Models for Systems-Level Engineering Assessment

• Published in: ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil Engineering Civil Engineering, 2021-06, Vol.7 (2)
• Main Authors: Stephenson, Victoria, Oates, Chris. J, Finlayson, Andrew, Thomas, Chris, Wilson, Kevin J
• Format: Article
• Language: English
• Subjects: Civil engineering; Decision making; End users; Robustness (mathematics); Statistical analysis; Statistical inference; Technical Papers; Uncertainty; Urban environments
• ISSN: 2376-7642; 2376-7642
• DOI: 10.1061/AJRUA6.0001116

AbstractSystems-level analysis of an engineered structure demands robust scientific and statistical protocols to assess model-driven conclusions that are often nontraditional and causal in their content. The formal mathematical, statistical, and philosophical foundations of causal inference on which such protocols are based are, nevertheless, not widely understood. The aims of this article are to (1) communicate the essentials of graph-based causal inference to the civil engineering community, (2) demonstrate how rigorous causal conclusions—and formal quantification of uncertainty regarding those conclusions—may be obtained in a typical engineered system application, and (3) discuss the value of this approach in the context of engineered system assessment. The concepts are illustrated via a river-weir ecosystem case study as an example of decision making for engineered systems in the built environment. In this setting, it is demonstrated how rigorous predictions can be made about the outcome of decisions that take a lack of prior knowledge about the system into account. The findings highlight to end users the value in applying this approach in providing quantitative probabilistic outputs that counter decision uncertainty at system level.