Detecting low-complexity unobserved causes

Details Detecting low-complexity unobserved causes Detecting low-complexity unobserved causes

2012-02-14

arxiv.org/abs/1202.3737v1

Computer Science

Learning

Scientific paper

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.

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