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A Kernel-Based Causal Learning Algorithm


Conference Paper


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

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1273496.1273604
Event Name: ICML 2007
Event Place: Corvallis, OR, USA

Address: New York, NY, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {A Kernel-Based Causal Learning Algorithm},
  author = {Sun, X. and Janzing, D. and Sch{\"o}lkopf, B. and Fukumizu, K.},
  booktitle = {Proceedings of the 24th International Conference on Machine Learning},
  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},
  month_numeric = {6}