Anna Drewek: A Linear Non-Gaussian Acyclic Model for Causal Discovery

Adviser: Prof. Dr. Marloes Maathuis

July 2010

Abstract:

The discovery of causal relationships between variables is important in many applications. Shimizu et al. proposed a method to discover the causal structure from observational data in linear non-Gaussian acyclic models, abbreviated by LiNGAM (see Shimizu et al. 2006). We analyze their approach and empirically test the strictness of non-Gaussianity byapproximating the Gaussian distribution with the t-distribution. Moreover, we compare the performance of the LiNGAM algorithm to that of the PC algorithm (Sprites et al. 2000). Finally, a combination of both algorithms is discussed (Hoyer et al. 2008) that enables the detection of causal structure in linear acyclic models with arbitrary distributions.

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