@inproceedings{4382eb6e77d34bbeb488e1cc69bab2cf,
title = "Identifying causal pathways with and without diagrams",
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.",
keywords = "causal inference, causal models, causal reasoning, directed graphs, indirect effects, networks, path models",
author = "Corter, {James E.} and Mason, {David L.} and Barbara Tversky and Nickerson, {Jeffrey V.}",
note = "Publisher Copyright: {\textcopyright} CogSci 2011.; 33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 ; Conference date: 20-07-2011 Through 23-07-2011",
year = "2011",
language = "English",
series = "Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011",
pages = "2715--2720",
editor = "Laura Carlson and Christoph Hoelscher and Shipley, {Thomas F.}",
booktitle = "Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011",
}