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Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions

2007

Conference Paper

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We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We present experiments with toy data where the quantitative results are consistent with our intuitive understanding of complexity and smoothness. Also in some examples with real-world data the probability measure corresponding to the true causal direction turned out to be less complex than those of the reversed order.

Author(s): Sun, X. and Janzing, D. and Schölkopf, B.
Book Title: Proceedings of the 15th European Symposium on Artificial Neural Networks
Journal: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)
Pages: 441-446
Year: 2007
Month: April
Day: 0
Editors: M Verleysen
Publisher: D-Side Publications

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

Event Name: ESANN 2007
Event Place: Brugge, Belgium

Address: Evere, Belgium
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{4454,
  title = {Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions},
  author = {Sun, X. and Janzing, D. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)},
  booktitle = {Proceedings of the 15th European Symposium on Artificial Neural Networks
  },
  pages = {441-446},
  editors = {M Verleysen},
  publisher = {D-Side Publications},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Evere, Belgium},
  month = apr,
  year = {2007},
  month_numeric = {4}
}