PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions
X. Sun, D. Janzing and B. Schölkopf
In: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN)(2007).

Abstract

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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4029
Deposited By:Bernhard Schölkopf
Deposited On:25 February 2008