PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Telling cause from effect based on high-dimensional observations
Dominik / DJ Janzing, Patrik / PH Hoyer and Bernhard / BS Schoelkopf
arXive 2009.

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 both the covariance matrix of the cause and the structure matrix mapping cause to the effect are independently chosen. The method works for 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:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:6830
Deposited By:Dominik Janzing
Deposited On:08 March 2010