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A Heuristic Partial-Correlation-Based Algorithm for Causal Relationship Discovery on Continuous Data

  • Zhenxing Wang
  • Laiwan Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

In this paper, we propose a heuristic partial-correlation- based (HP) algorithm to discover causal structures of Bayesian networks with continuous variables. There are two advantages of HP algorithm compared with existing ones: the first is that HP algorithm has a polynomial time complexity in the worst case, and the second HP algorithm can be applied to the data generated by linear simultaneous equation model, without assuming data following multivariate Gaussian distribution. Empirical results show that HP algorithm outperforms existing algorithms in both accuracy and run time.

Keywords

Bayesian Network Partial Correlation Causal Structure Multivariate Gaussian Distribution Conditional Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zhenxing Wang
    • 1
  • Laiwan Chan
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatin, N.T.Hong Kong

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