Time series: theory and methods
Time series: theory and methods
Multiple comparison procedures
Multiple comparison procedures
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Estimating Functions for Blind Separation When Sources Have Variance Dependencies
The Journal of Machine Learning Research
Finding a causal ordering via independent component analysis
Computational Statistics & Data Analysis
New permutation algorithms for causal discovery using ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Testing significance of mixing and demixing coefficients in ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Nonlinear independent component analysis with minimal nonlinear distortion
Proceedings of the 24th international conference on Machine learning
Detection of Unfaithfulness and Robust Causal Inference
Minds and Machines
Proceedings of the 25th international conference on Machine learning
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
International Journal of Approximate Reasoning
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes
Neural Information Processing
Causal Reasoning with Ancestral Graphs
The Journal of Machine Learning Research
ICA with Sparse Connections: Revisited
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Detecting the direction of causal time series
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A heuristic partial-correlation-based algorithm for causal relationship discovery on continuous data
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Bayesian discovery of linear acyclic causal models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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
On the identifiability of the post-nonlinear causal model
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
An efficient causal discovery algorithm for linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A causal discovery algorithm using multiple regressions
Pattern Recognition Letters
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
The Journal of Machine Learning Research
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
Assessing statistical reliability of LiNGAM via multiscale bootstrap
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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
Extensions of ICA for causality discovery in the hong kong stock market
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Graphical Methods, Inductive Causal Inference, and Econometrics: A Literature Review
Computational Economics
Sparse Linear Identifiable Multivariate Modeling
The Journal of Machine Learning Research
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
The Journal of Machine Learning Research
Information-geometric approach to inferring causal directions
Artificial Intelligence
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Discovering unconfounded causal relationships using linear non-gaussian models
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Linear non-Gaussian causal discovery from a composite set of major US macroeconomic factors
Expert Systems with Applications: An International Journal
Learning Causal Relations in Multivariate Time Series Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Towards integrative causal analysis of heterogeneous data sets and studies
The Journal of Machine Learning Research
Learning bayesian networks from Markov random fields: An efficient algorithm for linear models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning Bayesian network structure using Markov blanket decomposition
Pattern Recognition Letters
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
Learning linear cyclic causal models with latent variables
The Journal of Machine Learning Research
Multi-dimensional causal discovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data. Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuous-valued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-Gaussian distributions of non-zero variances. The solution relies on the use of the statistical method known as independent component analysis, and does not require any pre-specified time-ordering of the variables. We provide a complete Matlab package for performing this LiNGAM analysis (short for Linear Non-Gaussian Acyclic Model), and demonstrate the effectiveness of the method using artificially generated data and real-world data.