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Discovery of Causal Additive Models in the Presence of Unobserved Variables



Takashi Nicholas Maeda , Shohei Shimizu

* This is an extended version of the UAI 2021 paper entitled "Causal Additive Models with Unobserved Variables" 

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Causal Discovery with Multi-Domain LiNGAM for Latent Factors



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

* 9 pages, 5 figures 

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



Takashi Nicholas Maeda , Shohei Shimizu

* This is an extended version of the AISTATS 2020 paper entitled "RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders" 

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



Takashi Nicholas Maeda , Shohei Shimizu

* This is an extended version of the AISTATS 2020 paper 

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Analysis of Cause-Effect Inference via Regression Errors



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

* This is an extended version of the AISTATS 2018 paper. In this work, we provide detailed mathematical proofs and further extensive experiments 

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



Chao Li , Shohei Shimizu


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Estimation of interventional effects of features on prediction



Patrick Blöbaum , Shohei Shimizu

* To appear in Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017) 

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Error Asymmetry in Causal and Anticausal Regression



Patrick Blöbaum , Takashi Washio , Shohei Shimizu

* Behaviormetrika, 2017, 10.1007/s41237-017-0022-z 

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



Ricardo Silva , Shohei Shimizu

* 12 pages, 4 figures 

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



Shohei Shimizu , Aapo Hyvarinen , Yoshinobu Kawahara

* Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009) 

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