20 search results - page 1 / 4 » A Linear Non-Gaussian Acyclic Model for Causal Discovery |
JMLR
2006
13 years 4 months ago
2006
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data. Such methods make various assumptions on the data generating ...
ICONIP
2007
13 years 6 months ago
2007
Abstract. An effective way to examine causality is to conduct an experiment with random assignment. However, in many cases it is impossible or too expensive to perform controlled ...
JMLR
2010
12 years 11 months ago
2010
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...
UAI
2008
13 years 6 months ago
2008
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...
ICANN
2010
Springer
13 years 6 months ago
2010
Springer
Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations. However, modern datasets including gene expres...