Principal Investigator:
David Danks
Research Categories:
Psychological Foundations of Causal Judgment and Human Data-Mining

Project Description:
Data acquired from many disciplines, including epidemiology, cognitive neuroscience, and fault analysis on complex mechanical systems, frequently has underlying causal relationships. Causal Bayesian networks are a framework in which to represent such causal relationships. Using them, one can derive constraints for known causal relations or extract causal relations from new datasets. This tool has been applied to many fields, but only sporadically. Using these funds, Bayes net researchers and researchers in other fields, such as psychology and neuroimaging, will hold a series of workshops/meetings to explore the range of applications of Bayes nets. Viewing the world in terms of Bayesian networks can lead to wide changes in experimental methodology and data interpretation.