Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Shohei Shimizu

Shohei Shimizu

Discovery of Causal Additive Models in the Presence of Unobserved Variables



Takashi Nicholas Maeda , Shohei Shimizu

Figure 1 for Discovery of Causal Additive Models in the Presence of Unobserved Variables
Figure 2 for Discovery of Causal Additive Models in the Presence of Unobserved Variables
Figure 3 for Discovery of Causal Additive Models in the Presence of Unobserved Variables
Figure 4 for Discovery of Causal Additive Models in the Presence of Unobserved Variables
* This is an extended version of the UAI 2021 paper entitled "Causal Additive Models with Unobserved Variables" 
   Access Paper or Ask Questions

Causal Discovery with Multi-Domain LiNGAM for Latent Factors



Yan Zeng , Shohei Shimizu , Ruichu Cai , Feng Xie , Michio Yamamoto , Zhifeng Hao

Figure 1 for Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Figure 2 for Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Figure 3 for Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Figure 4 for Causal Discovery with Multi-Domain LiNGAM for Latent Factors
* 9 pages, 5 figures 
   Access Paper or Ask Questions

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders



Takashi Nicholas Maeda , Shohei Shimizu

Figure 1 for Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Figure 2 for Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Figure 3 for Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
Figure 4 for Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
* This is an extended version of the AISTATS 2020 paper entitled "RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders" 
   Access Paper or Ask Questions

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders



Takashi Nicholas Maeda , Shohei Shimizu

Figure 1 for RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Figure 2 for RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Figure 3 for RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Figure 4 for RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
* This is an extended version of the AISTATS 2020 paper 
   Access Paper or Ask Questions

Analysis of Cause-Effect Inference via Regression Errors



Patrick Blöbaum , Dominik Janzing , Takashi Washio , Shohei Shimizu , Bernhard Schölkopf

Figure 1 for Analysis of Cause-Effect Inference via Regression Errors
Figure 2 for Analysis of Cause-Effect Inference via Regression Errors
Figure 3 for Analysis of Cause-Effect Inference via Regression Errors
Figure 4 for Analysis of Cause-Effect Inference via Regression Errors
* This is an extended version of the AISTATS 2018 paper. In this work, we provide detailed mathematical proofs and further extensive experiments 
   Access Paper or Ask Questions

Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data



Chao Li , Shohei Shimizu

Figure 1 for Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
Figure 2 for Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
Figure 3 for Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data

   Access Paper or Ask Questions

Estimation of interventional effects of features on prediction



Patrick Blöbaum , Shohei Shimizu

Figure 1 for Estimation of interventional effects of features on prediction
Figure 2 for Estimation of interventional effects of features on prediction
* To appear in Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017) 
   Access Paper or Ask Questions

Error Asymmetry in Causal and Anticausal Regression



Patrick Blöbaum , Takashi Washio , Shohei Shimizu

Figure 1 for Error Asymmetry in Causal and Anticausal Regression
Figure 2 for Error Asymmetry in Causal and Anticausal Regression
Figure 3 for Error Asymmetry in Causal and Anticausal Regression
Figure 4 for Error Asymmetry in Causal and Anticausal Regression
* Behaviormetrika, 2017, 10.1007/s41237-017-0022-z 
   Access Paper or Ask Questions

Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms



Ricardo Silva , Shohei Shimizu

Figure 1 for Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Figure 2 for Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Figure 3 for Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Figure 4 for Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
* 12 pages, 4 figures 
   Access Paper or Ask Questions

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model



Shohei Shimizu , Aapo Hyvarinen , Yoshinobu Kawahara

Figure 1 for A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Figure 2 for A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Figure 3 for A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Figure 4 for A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
* Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009) 
   Access Paper or Ask Questions
1
2
3
>>