B1704
Title: TSLiNGAM: DirectLiNGAM under heavy tails
Authors: Sarah Leyder - University of Antwerp (Belgium) [presenting]
Tim Verdonck - KU Leuven and UAntwerpen - imec (Belgium)
Jakob Raymaekers - KU Leuven (Belgium)
Abstract: One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on their causes. The possible identifiability of SCM-given data depends on assumptions made on the noise variables and the functional classes in the SCM. For instance, in the LiNGAM model, the functional class is restricted to linear functions and the disturbances have to be non-Gaussian. TSLiNGAM is a new method proposed for identifying the DAG of a causal model based on observational data. TSLiNGAM builds on DirectLiNGAM, a popular algorithm which uses simple OLS regression for identifying causal directions between variables. TSLiNGAM leverages the non-Gaussianity assumption of the error terms in the LiNGAM model to obtain a more efficient and robust estimation of the causal structure. TSLiNGAM is justified theoretically and is studied empirically in an extensive simulation study. It performs significantly better on heavy-tailed and skewed data and demonstrates a high small-sample efficiency. In addition, TSLiNGAM also shows better robustness properties as it is more resilient to contamination.
