Detecting low-complexity unobserved causes
Machine Learning
2012-02-20 v1 Machine Learning
Abstract
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a "direct" causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a genetic marker that is correlated with a phenotype of interest, we want to detect whether this marker is causal or it only correlates with a causal one. Our method is based on the analysis of the location of the conditional distributions P(Y|x) in the simplex of all distributions of Y. We report encouraging results on semi-empirical data.
Keywords
Cite
@article{arxiv.1202.3737,
title = {Detecting low-complexity unobserved causes},
author = {Dominik Janzing and Eleni Sgouritsa and Oliver Stegle and Jonas Peters and Bernhard Schoelkopf},
journal= {arXiv preprint arXiv:1202.3737},
year = {2012}
}