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Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Kernel independent component analysis
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Learning the Structure of Linear Latent Variable Models
The Journal of Machine Learning Research
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
International Journal of Approximate Reasoning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Consistent Nonparametric Tests of Independence
The Journal of Machine Learning Research
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
The Journal of Machine Learning Research
Use of prior knowledge in a non-Gaussian method for learning linear structural equation models
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Measuring statistical dependence with hilbert-schmidt norms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Towards integrative causal analysis of heterogeneous data sets and studies
The Journal of Machine Learning Research
Estimating a causal order among groups of variables in linear models
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
The Journal of Machine Learning Research
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Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the data-generating process of variables. Recently, it was shown that use of non-Gaussianity identifies the full structure of a linear acyclic model, that is, a causal ordering of variables and their connection strengths, without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering and connection strengths based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model, that is, if all the model assumptions are met and the sample size is infinite.