Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Temporal causal modeling with graphical granger methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes
Neural Information Processing
A smoothed bootstrap test for independence based on mutual information
Computational Statistics & Data Analysis
Introduction to Causal Inference
The Journal of Machine Learning Research
Graphical Methods, Inductive Causal Inference, and Econometrics: A Literature Review
Computational Economics
| Hi-index | 0.03 |
The application of independent component analysis to discovery of a causal ordering between observed variables is studied. Path analysis is a widely-used method for causal analysis. It is of confirmatory nature and can provide statistical tests for assumed causal relations based on comparison of the implied covariance matrix with a sample covariance. However, it is based on the assumption of normality and only uses the covariance structure, which is why it has several problems, for example, one cannot find the causal direction between two variables if only those two variables are observed because the two models to be compared are equivalent to each other. A new statistical method for discovery of a causal ordering using non-normality of observed variables is developed to provide a partial solution to the problem.