Abstract
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.
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References
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press (2000)
Heckerman, D., Meek, C., Cooper, G.: A Bayesian Approach to Causal Discovery. Technical Report MSR-TR-97-05, Microsoft Research (1997)
Hoyer, P.O., et al.: Nonlinear Causal Discovery with Additive Noise Models. In: The Twenty-Second Annual Conference on Neural Information Processing Systems (2008)
Shimizu, S., et al.: A Linear Non-Gaussian Acyclic Model for Causal Discovery. The Journal of Machine Learning Research 7, 2003–2030 (2006)
Zhang, K., et al.: Kernel-based Conditional Independence Test and Application in Causal Discovery. arXiv preprint arXiv:1202.3775 (2012)
Cai, R.C., Zhang, Z.J., Hao, Z.F.: BASSUM: A Bayesian Semi-supervised Method for Classification Feature Selection. Pattern Recognition 44(4), 811–820 (2011)
Sun, X.H., Dominik, J., Bernhard, S.: Causal Inference by Choosing Graphs with Most Plausible Markov Kernels. In: Proceeding of the 9th Int. Symp. Art. Int. and Math., Fort Lauderdale, Florida (2006)
Gullberg, M., Noreus, K., Brattsand, G., et al.: Purification and Characterization of A 19-kilodalton Intracellular protein. An activation-regulated Putative Protein Kinase C Substrate of T lymphocytes. J. Biol. Chein. 265, 17499–17505 (1990)
Tang, L.-J., Jiang, J.-H., Wu, H.-L., et al.: Variable Selection Using Probability Density Function Similarity for Support Vector Machine Classification of High-dimensional Microarray Data. Talanta 79(2), 260–267 (2009)
Wang, X., Gotoh, O.: Accurate Molecular Classification of Cancer Using Simple Rules. BMC Med Genomics 2(64) (2009)
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Hao, Z., Huang, J., Cai, R., Wen, W. (2013). A Hybrid Approach for Large Scale Causality Discovery. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_1
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DOI: https://doi.org/10.1007/978-3-642-39678-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
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