Abstract
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.
Highlights
Alarge part of scientific research is driven by interest in discovering causal relationships from data to be used as guides for intervention, to maximise utility of interest and to minimise undesirable risk
These metrics are based on the confusion matrix parameters where True Positives (TP) is the number of true edges discovered in the generated graph, False Positives (FP) is the number of false edges discovered in the generated graph, True Negatives (TN) is the number of true direct independencies in the generated graph, and False Negatives (FN) is the number of false direct independencies in the generated graph
4 DISCUSSION AND CONCLUDING REMARKS The aim of the paper is not to demonstrate that temporal constraints are beneficial for BN structure learning
Summary
Alarge part of scientific research is driven by interest in discovering causal relationships from data to be used as guides for intervention, to maximise utility of interest and to minimise undesirable risk. Much of this research is based on methods that focus on maximising the predictive accuracy of a target variable X from a set of observed predictors Y. The best predictors of X are often not its causes and the motto association does not imply causation. While the distinction between association and causation is nowadays better understood, what has changed over the decades is mostly the way the results are stated rather than the way they are produced. Any model that captures cause-and-effect relationships must, by definition, adhere to the temporal order of the variables. An effect at time t can only have causes observed at a time prior to t. The question of how to most effectively develop such models to solve realworld problems is a current concern
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