Detecting low-complexity unobserved causes

摘要

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 ge netics. Given a genetic marker that is corre lated 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 sim plex of all distributions of Y. We report en couraging results on semi-empirical data.