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Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which isfurther complicated by many theoretical issues, such as the I-equivalence amongdifferent structures. In this work, we focus on a specific subclass of BNs,named Suppes-Bayes Causal Networks (SBCNs), which include specific structuralconstraints based on Suppes' probabilistic causation to efficiently modelcumulative phenomena. Here we compare the performance, via extensivesimulations, of various state-of-the-art search strategies, such as localsearch techniques and Genetic Algorithms, as well as of distinct regularizationmethods. The assessment is performed on a large number of simulated datasetsfrom topologies with distinct levels of complexity, various sample size anddifferent rates of errors in the data. Among the main results, we show that theintroduction of Suppes' constraints dramatically improve the inferenceaccuracy, by reducing the solution space and providing a temporal ordering onthe variables. We also report on trade-offs among different search techniquesthat can be efficiently employed in distinct experimental settings. Thismanuscript is an extended version of the paper "Structural Learning ofProbabilistic Graphical Models of Cumulative Phenomena" presented at the 2018International Conference on Computational Science.

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena