Finding Exogenous Variables in Data with Many More Variables than Observations

Details Finding Exogenous Variables in Data with Many More Variables than Observations Finding Exogenous Variables in Data with Many More Variables than Observations

2009-04-06

arxiv.org/abs/0904.0838v2

ARTIFICIAL NEURAL NETWORKS - ICANN 2010. Lecture Notes in Computer Science, 2010, Volume 6352/2010, 67-76

Statistics

Machine Learning

A revised version of this was published in Proc. ICANN2010

Scientific paper

10.1007/978-3-642-15819-3_10

Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p>n). In this paper, we propose a method to find exogenous variables in a linear non-Gaussian causal model, which requires much smaller sample sizes than conventional methods and works even when p>>n. The key idea is to identify which variables are exogenous based on non-Gaussianity instead of estimating the entire structure of the model. Exogenous variables work as triggers that activate a causal chain in the model, and their identification leads to more efficient experimental designs and better understanding of the causal mechanism. We present experiments with artificial data and real-world gene expression data to evaluate the method.

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