Estimation Of Causal Structures In Longitudinal Data Using Non-Gaussianity
Kento Kadowaki, Shohei Shimizu, Takashi Washio

Recently, there is a growing need for statistical learning of causal structures in data with many variables. A structural equation model called Linear Non-Gaussian Acyclic Model (LiNGAM) has been extensively studied to uniquely estimate causal structures in data. The key assumptions are that external influences are independent and follow non-Gaussian distributions. However, LiNGAM does not capture temporal structural changes in observed data. In this paper, we consider learning causal structures in longitudinal data that collects samples over a period of time. In previous studies of
LiNGAM, there was no model specialized to handle longitudinal data with multiple samples. Therefore, we propose a new model called longitudinal LiNGAM and a new estimation method using the information on temporal structural changes and non-Gaussianity of data. The new approach requires less assumptions than previous methods.