2013/03/06 by Marek J. Drużdżel, Marek J. Druzdzel, Herbert A. Simon +2 · 1 citation
Computer Science · Mathematics · #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian network #Bayesian probability #Causal model #Causality (physics) #Cognitive Science and Mapping #Computer science #Context (archaeology) #Domain (mathematical analysis) #Graphical model #Interpretation (philosophy) #Logic, Reasoning, and Knowledge #Machine learning #Mathematics #Statistics #Structural equation modeling #cs.AI
paper · pdf · doi:10.48550/arxiv.1303.1454
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)
arxiv created 2013/03/06 · openalex publication_date 2013/03/06 · arxiv updated 2013/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mechanisms are placed in the context of a system. We lay the link between structural equations models and BBNs models and formulate the conditions under which the latter can be given causal interpretation.