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Many statistical methods have been proposed to estimate causal models inclassical situations with fewer variables than observations (p<n, p: the numberof variables and n: the number of observations). However, modern datasetsincluding gene expression data need high-dimensional causal modeling inchallenging situations with orders of magnitude more variables thanobservations (p>>n). In this paper, we propose a method to find exogenousvariables in a linear non-Gaussian causal model, which requires much smallersample sizes than conventional methods and works even when p>>n. The key ideais to identify which variables are exogenous based on non-Gaussianity insteadof estimating the entire structure of the model. Exogenous variables work astriggers that activate a causal chain in the model, and their identificationleads to more efficient experimental designs and better understanding of thecausal mechanism. We present experiments with artificial data and real-worldgene expression data to evaluate the method.

Finding Exogenous Variables in Data with Many More Variables than Observations