Abstract
Telling cause from effect attracts various attentions from researchers recently. Here we study the algorithms proposed under the postulate of independence between cause and mechanisms. Firstly, we conduct a review of the different definitions of independence in different causal inference algorithms, and show how these theories could lead to practical methodologies. Then, we provide justifications about their links, showing how the seemingly different theoretical foundations could be integrated. This provides new insights of the relations between different inference algorithms, and gives readers a comprehensive understanding of the methods under this research topic.
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 subscriptionsReferences
Chaitin, G.J.: A theory of program size formally identical to information theory. J. ACM (JACM) 22(3), 329–340 (1975)
Hoyer, P.O., Janzing, D., Mooij, J.M., Peters, J.R., Schölkopf, B.: Nonlinear causal discovery with additive noise models. In: Advances in Neural Information Processing Systems, pp. 689–696 (2009)
Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., Steudel, B., Schölkopf, B.: Information-geometric approach to inferring causal directions. Artif. Intell. 182, 1–31 (2012)
Janzing, D., Scholkopf, B.: Causal inference using the algorithmic markov condition. IEEE Trans. Inf. Theory 56(10), 5168–5194 (2010)
Lemeire, J., Janzing, D.: Replacing causal faithfulness with algorithmic independence of conditionals. Mind. Mach. 23(2), 227–249 (2013)
Liu, F., Chan, L.: Causal inference on discrete data via estimating distance correlations. Neural Comput. 28(5), 801–814 (2016)
Mooij, J.M., Stegle, O., Janzing, D., Zhang, K., Schölkopf, B.: Probabilistic latent variable models for distinguishing between cause and effect. In: Advances in Neural Information Processing Systems, pp. 1687–1695 (2010)
Sun, X., Janzing, D., Schölkopf, B.: Causal reasoning by evaluating the complexity of conditional densities with kernel methods. Neurocomputing 71(7), 1248–1256 (2008)
Székely, G.J., Rizzo, M.L., Bakirov, N.K., et al.: Measuring and testing dependence by correlation of distances. Ann. Stat. 35(6), 2769–2794 (2007)
Zhang, K., Zhang, J., Schölkopf, B.: Distinguishing cause from effect based on exogeneity. arXiv preprint (2015). arXiv:1504.05651
Acknowledgments
The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administration Region, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, F., Chan, L. (2017). On the Relations of Theoretical Foundations of Different Causal Inference Algorithms. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-68935-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68934-0
Online ISBN: 978-3-319-68935-7
eBook Packages: Computer ScienceComputer Science (R0)