TY - GEN
T1 - Theory-Based Causal Inference
AU - Tenenbaum, Joshua B.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.
PY - 2002
Y1 - 2002
N2 - People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data - often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottom-up models of inference.
AB - People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data - often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottom-up models of inference.
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M3 - Conference contribution
AN - SCOPUS:85156274853
T3 - NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems
SP - 35
EP - 42
BT - NIPS 2002
A2 - Becker, Suzanna
A2 - Thrun, Sebastian
A2 - Obermayer, Klaus
PB - MIT Press Journals
T2 - 15th International Conference on Neural Information Processing Systems, NIPS 2002
Y2 - 9 December 2002 through 14 December 2002
ER -