DAGs are seen as a significant advancement in the realm of causal analysis by expanding the analysis boundaries and including essential influencers that are absent in other approaches. DAGs have proven their utility in application in mathematics, particularly graph theory, and computer science. Applying them to casual analysis enriches the practitioners ability to account for failure causes that historical record has revealed as being significant contributors. This overview will acquaint the attendee with DAGs and their potential contribution to the discipline of reliability.
October 8, 2020 @ 9:00 am – 10:00 am