Paper Info
Title
An Experimental Comparison Of Linear Non-Gaussian Causal Discovery Methods And Their Variants
Abstract
Many multivariate Gaussianity-based techniques for identifying causal networks of observed variables have been proposed. These methods have several problems such that they cannot uniquely identify the causal networks without any prior knowledge. To alleviate this problem, a non-Gaussianity-based identification method LiNGAM was proposed. Though the LiNGAM potentially identifies a unique causal network without using any prior knowledge, it needs to properly examine independence assumptions of the causal network and search the correct causal network by using finite observed data points only. On another front, a kernel based independence measure that evaluates the independence more strictly was recently proposed. In addition, some advanced generic search algorithms including beam search have been extensively studied in the past. In this paper, we propose some variants of the LiNGAM method which introduce the kernel based method and the beam search enabling more accurate causal network identification. Furthermore, we experimentally characterize the LiNGAM and its variants in terms of accuracy and robustness of their identification.
Year
DOI
Venue
2010
10.1109/IJCNN.2010.5596737
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010
Keywords
Field
DocType
kernel,search algorithms,mathematical model,causality,correlation,stochastic processes,accuracy,linear systems,statistical analysis,knowledge engineering,beam search,robustness,search algorithm
Data point,Kernel (linear algebra),Causality,Search algorithm,Computer science,Beam search,Robustness (computer science),Gaussian,Knowledge engineering,Artificial intelligence,Machine learning
Conference
ISSN
Citations 
PageRank 
2161-4393
5
0.54
References 
Authors
5
4
Name
Order
Citations
PageRank
Yasuhiro Sogawa1754.85
Shohei Shimizu249245.80
Kawahara, Yoshinobu331731.30
Takashi Washio41775190.58