PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Telling cause from effect based on high-dimensional observations
Dominik Janzing, Patrik Hoyer and Bernhard Schölkopf
In: ICML 2010(2010).

Abstract

We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if the covariance matrix of the cause and the structure matrix mapping the cause to the effect are independently chosen. The method applies to both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7671
Deposited By:Patrik Hoyer
Deposited On:17 March 2011