Telling cause from effect based on high-dimensional observations

Details Telling cause from effect based on high-dimensional observations Telling cause from effect based on high-dimensional observations

2009-09-24

arxiv.org/abs/0909.4386v1

Statistics

Machine Learning

13 pages, 5 figures

Scientific paper

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.

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