The importance of temporal information in Bayesian network structure learning

• Published in: Expert systems with applications Vol. 164; p. 113814
• Main Author: Constantinou, Anthony C.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.02.2021; Elsevier BV
• Subjects: Algorithms; Bayesian analysis; Causal discovery; Causal graphs; Directed acyclic graphs; Machine learning; Order-based learning; Probabilistic graphical models; Temporal constraints; Probabilistic graphical models; Order-based learning; Directed acyclic graphs; Causal discovery; Temporal constraints; Causal graphs
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113814

Several algorithms have been proposed towards discovering the graphical structure of Bayesian networks. Most of these algorithms are restricted to observational data and some enable us to incorporate knowledge as constraints in terms of what can and cannot be discovered by an algorithm. A common type of such knowledge involves the temporal order of the variables in the data. For example, knowledge that event B occurs after observing A and hence, the constraint that B cannot cause A. This paper investigates real-world case studies that incorporate interesting properties of objective temporal variable order, and the impact these temporal constraints have on the learnt graph. The results show that most of the learnt graphs are subject to major modifications after incorporating incomplete temporal objective information. Because temporal information is widely viewed as a form of knowledge that is subjective, rather than as a form of data that tends to be objective, it is generally disregarded and reduced to an optional piece of information that only few of the structure learning algorithms may consider. The paper argues that objective temporal information should form part of observational data, to reduce the risk of disregarding such information when available and to encourage its reusability across related studies.