Natural gradient works efficiently in learning
Neural Computation
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
The Fixed-Point Algorithm and Maximum Likelihood Estimation forIndependent Component Analysis
Neural Processing Letters
Kernel independent component analysis
The Journal of Machine Learning Research
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
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
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
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
The Journal of Machine Learning Research
Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors
Expert Systems with Applications: An International Journal
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 a causal ordering of variables in a linear acyclic model 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 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.