Abstract
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithms proposed by Dai et al. has demonstrated the ability of the Minimum Message Length (MML) principle in discovering Linear Causal Models from training data. In order to further explore ways to improve efficiency, this paper incorporates the Hoeffding Bounds into the learning process. At each step of causal discovery, if a small number of data items is enough to distinguish the better model from the rest, the computation cost will be reduced by ignoring the other data items. Experiments with data set from related benchmark models indicate that the new algorithm achieves speedup over previous work in terms of learning efficiency while preserving the discovery accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wallace, C., Korb, K., Dai, H.: Causal discovery via MML. In: Proceedings of the 13th International Conference on Machine Learning (ICML 1996), pp. 516–524 (1996)
Li, G., Dai, H., Tu, Y.: Identifying markov blankets using lasso estimation. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 308–318. Springer, Heidelberg (2004)
Bollen, K.: Structural Equations with Latent Variables. Wiley, New York (1989)
Wallace, C., Boulton, D.: An information measure for classification. Computer Journal 11, 185–194 (1968)
Dai, H., Korb, K., Wallace, C., Wu, X.: A study of causal discovery with small samples and weak links. In: Proceedings of the 15th International Joint Conference On Artificial Intelligence IJCAI 1997, pp. 1304–1309. Morgan Kaufmann Publishers, Inc., San Francisco (1997)
Dai, H., Li, G.: An improved approach for the discovery of causal models via MML. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 304–315. Springer, Heidelberg (2002)
Li, G., Dai, H., Tu, Y.: Linear causal model discovery using MML criterion. In: Proceedings of 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 274–281. IEEE Computer Society, Los Alamitos (2002)
Dai, H., Li, G., Zhou, Z.H.: Ensembling causal discovery. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 260–271. Springer, Heidelberg (2004)
Maron, O., Moore, A.W.: Hoeffding races: Accelerating model selection search for classiffication and function approximation. In: Advances in Neural Information Processing Systems, Providence, RI, vol. 6, pp. 59–66. Morgan Kaufmann, San Francisco (1994)
Hoeffding, W.: Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58, 13–30 (1963)
Maron, O., Moore, A.W.: The racing algorithm: Model selection for lazy learners. Artificial Intelligence Review 11, 193–225 (1997)
Dai, H., Li, G., Tu, Y.: An empirical study of encoding schemes and search strategies in discovering causal networks. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 48–59. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, G., Dai, H., Tu, Y., Kurt, T. (2004). Accelerating Linear Causal Model Discovery Using Hoeffding Bounds. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-28633-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
eBook Packages: Springer Book Archive