Causal relevance

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationReasoning with Uncertainty in Robotics - International Workshop, RUR 1995, Proceedings
EditorsMichiel van Lambalgen, Frans Voorbraak, Leo Dorst
PublisherSpringer Verlag
Number of pages22
ISBN (Print)3540613765, 9783540613763
StatePublished - 1996
EventInternational Workshop on Reasoning with Uncertainty in Robotics, RUR 1995 - Amsterdam, Netherlands
Duration: Dec 4 1995Dec 6 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Workshop on Reasoning with Uncertainty in Robotics, RUR 1995

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


  • Bayes net
  • Causality
  • Probability tree
  • Refinement
  • Relevance
  • Sign
  • Simplification
  • Tracking


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