Identifying causal pathways with and without diagrams

James E. Corter, David L. Mason, Barbara Tversky, Jeffrey V. Nickerson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Causal modeling generally involves the construction and use of diagrammatic representations of the causal assumptions, expressed as directed acyclic graphs (DAGs). Do such graphs have cognitive benefits, for example by facilitating user inferences involving the underlying causal models? In two empirical studies, participants were given a set of causal assumptions, then attempted to identify all the causal pathways linking two variables in the model implied by these causal assumptions. Participants who were provided with a path diagram expressing the assumptions were more successful at identifying indirect pathways than those given the assumptions in the form of lists. Furthermore, the spatial orientation of the causal flow in the graphical model (left to right or right to left) had effects on the speed and accuracy with which participants made these inferences.

Original languageEnglish
Title of host publicationExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
Pages2715-2720
Number of pages6
ISBN (Electronic)9780976831877
StatePublished - 2011
Event33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 - Boston, United States
Duration: 20 Jul 201123 Jul 2011

Publication series

NameExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Conference

Conference33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Country/TerritoryUnited States
CityBoston
Period20/07/1123/07/11

Keywords

  • causal inference
  • causal models
  • causal reasoning
  • directed graphs
  • indirect effects
  • networks
  • path models

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