A Hybrid Approach for Large Scale Causality Discovery

  • Zhifeng Hao
  • Jinlong Huang
  • Ruichu Cai
  • Wen Wen
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)


Causality discovery is one of the basic problems in the scientific field. Though many researchers are committed to find the causal relation from observational data, there are still no effective methods for the high dimensional data. In this work, we propose a hybrid approach by taking the advantage of two state of the art causal discovery methods. In the proposed method, the structure learning based methods are explored to discover the causal skeleton, and then the additive noise models are conducted to distinguish the direction of causalities. The experimental results show that the proposed approach is effective and scalable for the large scale causality discovery problems.


Nonlinear causality Causal Markov Assumption additive noise model 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhifeng Hao
    • 1
    • 2
  • Jinlong Huang
    • 1
  • Ruichu Cai
    • 2
  • Wen Wen
    • 2
  1. 1.Faculty of Applied MathematicsGuangdong University of TechnologyGuangzhouChina
  2. 2.Faculty of Computer ScienceGuangdong University of TechnologyGuangzhouChina

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