We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm.
| Author(s): | Sun, X. and Janzing, D. and Schölkopf, B. and Fukumizu, K. |
| Book Title: | Proceedings of the 24th International Conference on Machine Learning |
| Pages: | 855-862 |
| Year: | 2007 |
| Month: | June |
| Day: | 0 |
| Editors: | Z Ghahramani |
| Publisher: | ACM Press |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | New York, NY, USA |
| DOI: | 10.1145/1273496.1273604 |
| Event Name: | ICML 2007 |
| Event Place: | Corvallis, OR, USA |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{4457,
title = {A Kernel-Based Causal Learning Algorithm},
booktitle = {Proceedings of the 24th International Conference on Machine Learning},
abstract = {We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm.},
pages = {855-862},
editors = {Z Ghahramani},
publisher = {ACM Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {New York, NY, USA},
month = jun,
year = {2007},
slug = {4457},
author = {Sun, X. and Janzing, D. and Sch{\"o}lkopf, B. and Fukumizu, K.},
month_numeric = {6}
}