Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Independent component analysis: algorithms and applications
Neural Networks
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Identifying confounders using additive noise models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Discovery of exogenous variables in data with more variables than observations
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
The Journal of Machine Learning Research
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
The Journal of Machine Learning Research
| Hi-index | 0.00 |
LiNGAM has been successfully applied to casual inferences of some real world problems. Nevertheless, basic LiNGAM assumes that there is no latent confounder of the observed variables, which may not hold as the confounding effect is quite common in the real world. Causal discovery for LiNGAM in the presence of latent confounders is a more significant and challenging problem. In this paper, we propose a cumulant-based approach to the pairwise causal discovery for LiNGAM in the presence of latent confounders. The method assumes that the latent confounder is Gaussian distributed and statistically independent of the disturbances. We give a theoretical proof that in the presence of latent Gaussian confounders, the causal direction of the observed variables is identifiable under the mild condition that the disturbances are both super-gaussian or sub-gaussian. Experiments on synthesis data and real world data have been conducted to show the effectiveness of our proposed method.