Theory-based causal inference

Joshua B. Tenenbaum; Thomas L. Griffiths

Theory-based causal inference

Joshua B. Tenenbaum, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
Publisher	Neural information processing systems foundation
ISBN (Print)	0262025507, 9780262025508
State	Published - 2003
Externally published	Yes
Event	16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada Duration: Dec 9 2002 → Dec 14 2002

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	16th Annual Neural Information Processing Systems Conference, NIPS 2002
Country/Territory	Canada
City	Vancouver, BC
Period	12/9/02 → 12/14/02

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Theory-based causal inference",

abstract = "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.",

author = "Tenenbaum, {Joshua B.} and Griffiths, {Thomas L.}",

year = "2003",

language = "English (US)",

isbn = "0262025507",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002",

note = "16th Annual Neural Information Processing Systems Conference, NIPS 2002 ; Conference date: 09-12-2002 Through 14-12-2002",

}

Tenenbaum, JB & Griffiths, TL 2003, Theory-based causal inference. in Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 16th Annual Neural Information Processing Systems Conference, NIPS 2002, Vancouver, BC, Canada, 12/9/02.

Theory-based causal inference. / Tenenbaum, Joshua B.; Griffiths, Thomas L.
Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002. Neural information processing systems foundation, 2003. (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Theory-based causal inference

AU - Tenenbaum, Joshua B.

AU - Griffiths, Thomas L.

PY - 2003

Y1 - 2003

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:84899033090

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SN - 9780262025508

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002

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ER -

Theory-based causal inference

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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