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A Bayesian approach to learning causal networks

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David HeckermanAuthors Info & Claims

UAI'95: Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Pages 285 - 295

Published: 18 August 1995 Publication History

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Abstract

Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component independence. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.

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Cited By

Dhir APower SVan Der Wilk MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Bivariate causal discovery using Bayesian model selectionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692496(10710-10735)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692496
Choo DGouleakis TBhattacharyya AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Active causal structure learning with adviceProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618641(5838-5867)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618641
Toth CLorch LKnoll CKrause APernkopf FPeharz Rvon Kügelgen JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Active Bayesian causal inferenceProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601453(16261-16275)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601453
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Index Terms

A Bayesian approach to learning causal networks
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Published In

cover image Guide Proceedings

UAI'95: Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

August 1995

590 pages

ISBN:1558603859

Editors:
Philippe Besnard
IRISA, Rennes, France
,
Steve Hanks
University of Washington, Seattle

Sponsors

Rockwell Science Center: Rockwell Science Center
Lumina Decision Systems: Lumina Decision Systems, Inc.

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 18 August 1995

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Cited By

Dhir APower SVan Der Wilk MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Bivariate causal discovery using Bayesian model selectionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692496(10710-10735)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692496
Choo DGouleakis TBhattacharyya AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Active causal structure learning with adviceProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618641(5838-5867)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618641
Toth CLorch LKnoll CKrause APernkopf FPeharz Rvon Kügelgen JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Active Bayesian causal inferenceProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601453(16261-16275)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601453
Fire AZhu S(2015)Learning Perceptual Causality from VideoACM Transactions on Intelligent Systems and Technology10.1145/28097827:2(1-22)Online publication date: 26-Nov-2015
https://dl.acm.org/doi/10.1145/2809782
Adabor EAcquaah-Mensah GOduro F(2015)SAGAJournal of Biomedical Informatics10.1016/j.jbi.2014.08.01053:C(27-35)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.jbi.2014.08.010
Acharya SLee B(2013)Fast Causal Network Inference over Event StreamsProceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 805710.1007/978-3-642-40131-2_19(222-235)Online publication date: 26-Aug-2013
https://dl.acm.org/doi/10.1007/978-3-642-40131-2_19
Lee SLee K(2012)Context-prediction performance by a dynamic Bayesian networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.10.02639:5(4908-4914)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.10.026
Lee WYang K(2008)A clustering-based approach for inferring recurrent neural networks as gene regulatory networksNeurocomputing10.1016/j.neucom.2007.07.02371:4-6(600-610)Online publication date: 1-Jan-2008
https://dl.acm.org/doi/10.1016/j.neucom.2007.07.023
Mani SSpirtes PCooper G(2006)A theoretical study of Y structures for causal discoveryProceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence10.5555/3020419.3020458(314-323)Online publication date: 13-Jul-2006
https://dl.acm.org/doi/10.5555/3020419.3020458
Kang CTian J(2006)Inequality constraints in causal models with hidden variablesProceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence10.5555/3020419.3020448(233-240)Online publication date: 13-Jul-2006
https://dl.acm.org/doi/10.5555/3020419.3020448
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