2012/06/13 by Ulf Holm Nielsen, Ulf Nielsen, Jean‐Philippe Pellet +5
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1206.3276
Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)
arxiv created 2012/06/13 · openalex publication_date 2012/06/13 · arxiv updated 2012/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.