Pairwise likelihood ratios for estimation of non-Gaussian structural equation models

@article{Hyvrinen2013PairwiseLR,
title={Pairwise likelihood ratios for estimation of non-Gaussian structural equation models},
author={Aapo Hyv{\"a}rinen and Stephen M. Smith},
journal={Journal of Machine Learning Research},
year={2013},
volume={14},
pages={111-152}
}

Published 2013 in Journal of Machine Learning Research

We present new measures of the causal direction, or direction of effect, between two non-Gaussian random variables. They are based on the likelihood ratio under the linear non-Gaussian acyclic model (LiNGAM). We also develop simple first-order approximations of the likelihood ratio and analyze them based on related cumulant-based measures, which can be shown to find the correct causal directions. We show how to apply these measures to estimate LiNGAM for more than two variables, and even in the… CONTINUE READING