TY - GEN
T1 - Structure learning in human causal induction
AU - Tenenbaum, Joshua B.
AU - Griffiths, Thomas L.
PY - 2001
Y1 - 2001
N2 - We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayesian inference. Our argument is supported through the discussion of three data sets.
AB - We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayesian inference. Our argument is supported through the discussion of three data sets.
UR - http://www.scopus.com/inward/record.url?scp=84898969519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84898969519&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898969519
SN - 0262122413
SN - 9780262122412
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000
PB - Neural information processing systems foundation
T2 - 14th Annual Neural Information Processing Systems Conference, NIPS 2000
Y2 - 27 November 2000 through 2 December 2000
ER -