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06/04/2021
Discovery of Causal Additive Models in the Presence of Unobserved Variables
Causal discovery from data affected by unobserved variables is an import...
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09/19/2020
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Discovering causal structures among latent factors from observed data is...
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01/13/2020
Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Causal discovery from data affected by latent confounders is an importan...
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01/13/2020
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Causal discovery from data affected by latent confounders is an importan...
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02/19/2018
Analysis of Cause-Effect Inference via Regression Errors
We address the problem of inferring the causal relation between two vari...
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02/16/2018
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
Estimating causal models from observational data is a crucial task in da...
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09/03/2017
Estimation of interventional effects of features on prediction
The interpretability of prediction mechanisms with respect to the underl...
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10/11/2016
Error Asymmetry in Causal and Anticausal Regression
It is generally difficult to make any statements about the expected pred...
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11/09/2015
Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Learning a causal effect from observational data is not straightforward,...
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08/09/2014
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Structural equation models and Bayesian networks have been widely used t...
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08/02/2014
A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
A large amount of observational data has been accumulated in various fie...
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01/22/2014
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Discovering causal relations among observed variables in a given data se...
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01/22/2014
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
The notion of causality is used in many situations dealing with uncertai...
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03/29/2013
ParceLiNGAM: A causal ordering method robust against latent confounders
We consider learning a causal ordering of variables in a linear non-Gaus...
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08/21/2012
Learning LiNGAM based on data with more variables than observations
A very important topic in systems biology is developing statistical meth...
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07/04/2012
Discovery of non-gaussian linear causal models using ICA
In recent years, several methods have been proposed for the discovery of...
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06/13/2012
Causal discovery of linear acyclic models with arbitrary distributions
An important task in data analysis is the discovery of causal relationsh...
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04/09/2012
Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders
We consider to learn a causal ordering of variables in a linear non-Gaus...
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02/14/2012
Discovering causal structures in binary exclusive-or skew acyclic models
Discovering causal relations among observed variables in a given data se...
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04/28/2011
Joint estimation of linear non-Gaussian acyclic models
A linear non-Gaussian structural equation model called LiNGAM is an iden...
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01/13/2011
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Structural equation models and Bayesian networks have been widely used t...
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09/16/2009