We present new measures of the causal direction between two non-gaussian random variables. They are based on the likelihood ratio under the linear non-gaussian acyclic model (LiNG...
We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, conti...
Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Pat...
We consider causally sufficient acyclic causal models in which the relationship among the variables is nonlinear while disturbances have linear effects, and show that three princi...
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by...