Year: 2011
Type: article
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.
Authors: James E. Corter, David L. Mason, Barbara Tversky, Jeffrey V. Nickerson
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2573188399