
AbstractJudea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it is possible to achieve a compact representation of extended causal models.
FOS: Computer and information sciences, 330, Computer Science - Artificial Intelligence, Bayes Theorem, Compact representation, Models, Psychological, Causality, Normal, Bayesian network, Artificial Intelligence (cs.AI), Typical, Humans
FOS: Computer and information sciences, 330, Computer Science - Artificial Intelligence, Bayes Theorem, Compact representation, Models, Psychological, Causality, Normal, Bayesian network, Artificial Intelligence (cs.AI), Typical, Humans
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 13 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
