Causal inference on discrete data via estimating distance correlations

Neural Computation

Volumn 28, Issue 5, 2016, Pages 801-814

  Liu, Furui ^a   Chan, Laiwan ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84964070361     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00820     Document Type: Article

References (25)

1. Chen, Z., Zhang, K., & Chan, L. (2013). Nonlinear causal discovery for high dimensional data:Akernelized tracemethod. In Proceedings of the IEEE 13th International Conference on Data Mining (pp. 1003-1008). Piscataway, NJ: IEEE.
2. Chen, Z., Zhang, K., Chan, L., & Schölkopf, B. (2014). Causal discovery via reproducing kernel Hilbert space embeddings. Neural Computation, 26(7), 1484-1517.
3. Gretton, A., Herbrich, R., Smola, A., Bousquet, O., & Schölkopf, B. (2005). Kernel methods for measuring independence. Journal of Machine Learning Research, 6, 2075-2129.
4. Hoyer, P. O., & Hyttinen, A. (2009). Bayesian discovery of linear acyclic causal models. In Proceedings of the 25th International Conference on Uncertainty in Artificial Intelligence (pp. 240-248). Corvallis, OR: AUAI Press.
5. Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J. R., & Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems, 21 (pp. 689-696). Cambridge, MA: MIT Press.
6. Hyttinen, A., Eberhardt, F.,&Hoyer, P.O. (2012). Learning linear cyclic causal models with latent variables. Journal of Machine Learning Research, 13, 3387-3439.
7. Hyvärinen, A., & Smith, S. M. (2013). Pairwise likelihood ratios for estimation of non-gaussian structural equation models. Journal of Machine Learning Research, 14, 111-152.
8. Hyvärinen, A., Zhang, K., Shimizu, S.,&Hoyer,P. O. (2010). Estimation of a structural vector autoregression model using non-gaussianity. Journal of Machine Learning Research, 11, 1709-1731.
9. Janzing, D., Hoyer, P., & Schölkopf, B. (2010). Telling cause from effect based on high-dimensional observations. In Proceedings of the 27th International Conference on Machine Learning (pp. 479-486). Madison,WI: Omnipress.
10. Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniᅭsis, P., . . . Schölkopf, B. (2012). Information-geometric approach to inferring causal directions. Artificial Intelligence, 182, 1-31.
11. Janzing, D., & Schölkopf, B. (2010). Causal inference using the algorithmic Markov condition. IEEE Transactions on Information Theory, 56(10), 5168-5194.
12. Lemeire, J., & Janzing, D. (2013). Replacing causal faithfulness with algorithmic independence of conditionals. Minds and Machines, 23(2), 227-249.
13. Mooij, J. M., Janzing, D., Heskes, T., & Schölkopf, B. (2011). On causal discovery with cyclic additive noise models. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, & K. Q. Weinberger. (Eds.), Advances in neural information processing systems, 24 (pp. 639-647), Red Hook, NY: Curran.
14. Pearl, J. (2000). Causality: models, reasoning and inference. Cambridge: Cambridge University Press.
15. Peters, J., Janzing, D., & Schölkopf, B. (2010). Identifying cause and effect on discrete data using additive noise models. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (pp. 597-604). JMLR.
16. Peters, J., Janzing, D., & Scholkopf, B. (2011). Causal inference on discrete data using additive noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2436-2450.
17. Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., & Mooij, J. (2012). On causal and anticausal learning. In Proceedings of the 29th International Conference on Machine Learning (pp. 1-8). Madison,WI: Omnipress.
18. Sgouritsa, E., Janzing, D., Hennig, P., & Schölkopf, B. (2015). Inference of cause and effect with unsupervised inverse regression. In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (pp. 847-855). JMLR.
19. Shimizu, S.,Hoyer, P.O.,Hyvärinen, A.,&Kerminen, A. (2006). Alinear nongaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7, 2003-2030.
20. Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y.,Washio, . . . Bollen, K. (2011). Directlingam: A direct method for learning a linear non-gaussian structural equation model. Journal of Machine Learning Research, 12, 1225-1248.
21. Spirtes, P., Glymour, C. N., & Scheines, R. (2000). Causation, prediction, and search. Cambridge, MA: MIT Press.
22. Székely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007). Measuring and testing dependence by correlation of distances. Annals of Statistics, 35(6), 2769-2794.
23. Zhang, K., & Hyvärinen, A. (2009a). Causality discoverywith additive disturbances: An information-theoretical perspective. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 570-585). New York: Springer.
24. Zhang, K., & Hyvärinen, A. (2009b). On the identifiability of the post-nonlinear causal model. In Proceedings of the 25th International Conference on Uncertainty in Artificial Intelligence (pp. 647-655). Corvallis, OR: AUAI Press.
25. Zscheischler, J., Janzing, D., & Zhang, K. (2011). Testing whether linear equations are causal: A free probability theory approach. In Proceedings of the 27th International Conference on Uncertainty in Artificial Intelligence (pp. 839-847). Corvallis, OR: AUAI Press.