@inproceedings{7346a74ea68b4f9a80c972479b9f5a90,
title = "Causal relevance",
abstract = "Concepts of causal relevance and irrelevance are readily formulated in the context of Bayes nets, but these formulations have significant shortcomings. Most importantly, they do not allow for the great variety that can be observed in the temporal configuration of causally related entities. For example, they deal awkwardly with progressive causation, where continued action of a cause continues to enhance an effect. This article discusses how such subtleties can be handled when we look beyond Bayes nets to a more fundamental structure: nature's probability tree.",
keywords = "Bayes net, Causality, Probability tree, Refinement, Relevance, Sign, Simplification, Tracking",
author = "Glenn Shafer",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1996.; International Workshop on Reasoning with Uncertainty in Robotics, RUR 1995 ; Conference date: 04-12-1995 Through 06-12-1995",
year = "1996",
doi = "10.1007/BFb0013960",
language = "English (US)",
isbn = "3540613765",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "187--208",
editor = "{van Lambalgen}, Michiel and Frans Voorbraak and Leo Dorst",
booktitle = "Reasoning with Uncertainty in Robotics - International Workshop, RUR 1995, Proceedings",
address = "Germany",
}