Discovery of Non-gaussian Linear Causal Models using ICA. - Shohei Shimizu; Aapo Hyvärinen; Yutaka Kano; Patrik O. Hoyer (UAI, 2005)

New Permutation Algorithms for Causal Discovery Using ICA. - Patrik O. Hoyer; Shohei Shimizu; Aapo Hyvärinen; Yutaka Kano; Antti J. Kerminen (ICA, 2006)

Testing Significance of Mixing and Demixing Coefficients in ICA. - Shohei Shimizu; Aapo Hyvärinen; Yutaka Kano; Patrik O. Hoyer; Antti J. Kerminen (ICA, 2006)

A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis. - Aapo Hyvärinen; Shohei Shimizu (ICANN (2), 2006)

Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. - Patrik O. Hoyer; Shohei Shimizu; Antti J. Kerminen (Probabilistic Graphical Models, 2006)

Finding a causal ordering via independent component analysis. - Shohei Shimizu; Aapo Hyvärinen; Patrik O. Hoyer; Yutaka Kano (Comput. Stat. Data Anal., 2006)

A Linear Non-Gaussian Acyclic Model for Causal Discovery. - Shohei Shimizu; Patrik O. Hoyer; Aapo Hyvärinen; Antti J. Kerminen (J. Mach. Learn. Res., 2006)

Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes. - Shohei Shimizu; Aapo Hyvärinen (ICONIP (1), 2007)

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. - Aapo Hyvärinen; Shohei Shimizu; Patrik O. Hoyer (ICML, 2008)

Causal discovery of linear acyclic models with arbitrary distributions. - Patrik O. Hoyer; Aapo Hyvärinen; Richard Scheines; Peter Spirtes; Joseph D. Ramsey; Gustavo Lacerda; Shohei Shimizu (UAI, 2008)

Estimation of causal effects using linear non-Gaussian causal models with hidden variables. - Patrik O. Hoyer; Shohei Shimizu; Antti J. Kerminen; Markus Palviainen (Int. J. Approx. Reason., 2008)

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. - Shohei Shimizu; Aapo Hyvärinen; Yoshinobu Kawahara (UAI, 2009)

Estimation of linear non-Gaussian acyclic models for latent factors. - Shohei Shimizu; Patrik O. Hoyer; Aapo Hyvärinen (Neurocomputing, 2009)

Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models. - Takanori Inazumi; Shohei Shimizu; Takashi Washio (LVA/ICA, 2010)

Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap. - Yusuke Komatsu; Shohei Shimizu; Hidetoshi Shimodaira (ICANN (3), 2010)

Discovery of Exogenous Variables in Data with More Variables Than Observations. - Yasuhiro Sogawa; Shohei Shimizu; Aapo Hyvärinen; Takashi Washio; Teppei Shimamura; Seiya Imoto (ICANN (1), 2010)

An experimental comparison of linear non-Gaussian causal discovery methods and their variants. - Yasuhiro Sogawa; Shohei Shimizu; Yoshinobu Kawahara; Takashi Washio (IJCNN, 2010)

GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables - Yoshinobu Kawahara; Kenneth Bollen; Shohei Shimizu; Takashi Washio (CoRR, 2010)

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. - Aapo Hyvärinen; Kun Zhang 001; Shohei Shimizu; Patrik O. Hoyer (J. Mach. Learn. Res., 2010)

Discovering causal structures in binary exclusive-or skew acyclic models. - Takanori Inazumi; Takashi Washio; Shohei Shimizu; Joe Suzuki; Akihiro Yamamoto; Yoshinobu Kawahara (UAI, 2011)

Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. - Yoshinobu Kawahara; Shohei Shimizu; Takashi Washio (Neurocomputing, 2011)

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. - Shohei Shimizu; Takanori Inazumi; Yasuhiro Sogawa; Aapo Hyvärinen; Yoshinobu Kawahara; Takashi Washio; Patrik O. Hoyer; Kenneth Bollen (J. Mach. Learn. Res., 2011)

Estimating exogenous variables in data with more variables than observations. - Yasuhiro Sogawa; Shohei Shimizu; Teppei Shimamura; Aapo Hyvärinen; Takashi Washio; Seiya Imoto (Neural Networks, 2011)

Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. - Tatsuya Tashiro; Shohei Shimizu; Aapo Hyvärinen; Takashi Washio (ICANN (1), 2012)

Bootstrap Confidence Intervals in DirectLiNGAM. - Kittitat Thamvitayakul; Shohei Shimizu; Tsuyoshi Ueno; Takashi Washio; Tatsuya Tashiro (ICDM Workshops, 2012)

Joint estimation of linear non-Gaussian acyclic models. - Shohei Shimizu (Neurocomputing, 2012)

Estimation of causal structures in longitudinal data using non-Gaussianity. - Kento Kadowaki; Shohei Shimizu; Takashi Washio (MLSP, 2013)

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. - Takanori Inazumi; Takashi Washio; Shohei Shimizu; Joe Suzuki; Akihiro Yamamoto; Yoshinobu Kawahara (CoRR, 2014)

Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. - Shohei Shimizu; Kenneth Bollen (J. Mach. Learn. Res., 2014)

ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. - Tatsuya Tashiro; Shohei Shimizu; Aapo Hyvärinen; Takashi Washio (Neural Comput., 2014)

Discriminative and Generative Models in Causal and Anticausal Settings. - Patrick Blöbaum; Shohei Shimizu; Takashi Washio (AMBN@JSAI-isAI, 2015)

A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes. - Shohei Shimizu (AMBN@JSAI-isAI, 2015)

Error Asymmetry in Causal and Anticausal Regression. - Patrick Blöbaum; Takashi Washio; Shohei Shimizu (CoRR, 2016)

A novel principle for causal inference in data with small error variance. - Patrick Blöbaum; Shohei Shimizu; Takashi Washio (ESANN, 2017)

Estimation of interventional effects of features on prediction. - Patrick Blöbaum; Shohei Shimizu (MLSP, 2017)

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. - Ricardo Silva 001; Shohei Shimizu (J. Mach. Learn. Res., 2017)

Cause-Effect Inference by Comparing Regression Errors. - Patrick Blöbaum; Dominik Janzing; Takashi Washio; Shohei Shimizu; Bernhard Schölkopf (AISTATS, 2018)

A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. - Weimin Li 001; Heng Zhu; Xiaokang Zhou; Shohei Shimizu; Mingjun Xin; Qun Jin (DASC/PiCom/DataCom/CyberSciTech, 2018)

Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data. - Chao Li; Shohei Shimizu (CoRR, 2018)

Analysis of Cause-Effect Inference via Regression Errors. - Patrick Blöbaum; Dominik Janzing; Takashi Washio; Shohei Shimizu; Bernhard Schölkopf (CoRR, 2018)

Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization. - Weimin Li 001; Xiaokang Zhou; Shohei Shimizu; Mingjun Xin; Jiulei Jiang; Honghao Gao; Qun Jin (IEEE Access, 2019)

Analysis of cause-effect inference by comparing regression errors. - Patrick Blöbaum; Dominik Janzing; Takashi Washio; Shohei Shimizu; Bernhard Schölkopf (PeerJ Comput. Sci., 2019)

Multi-Modality Behavioral Influence Analysis for Personalized Recommendations in Health Social Media Environment. - Xiaokang Zhou; Wei Liang 006; Kevin I-Kai Wang; Shohei Shimizu (IEEE Trans. Comput. Soc. Syst., 2019)

RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. - Takashi Nicholas Maeda; Shohei Shimizu (AISTATS, 2020)

Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. - Kento Uemura; Shohei Shimizu (ICASSP, 2020)

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. - Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2020)

Causal Discovery with Multi-Domain LiNGAM for Latent Factors. - Yan Zeng 002; Shohei Shimizu; Ruichu Cai; Feng Xie 002; Michio Yamamoto; Zhifeng Hao (IJCAI, 2021)

Estimating individual-level optimal causal interventions combining causal models and machine learning models. - Keisuke Kiritoshi; Tomonori Izumitani; Kazuki Koyama; Tomomi Okawachi; Keisuke Asahara; Shohei Shimizu (CD@KDD, 2021)

Causal additive models with unobserved variables. - Takashi Nicholas Maeda; Shohei Shimizu (UAI, 2021)

Discovery of Causal Additive Models in the Presence of Unobserved Variables. - Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2021)

Privacy preservation in permissionless blockchain: A survey. - Li Peng; Wei Feng 010; Zheng Yan 002; Yafeng Li; Xiaokang Zhou; Shohei Shimizu (Digit. Commun. Networks, 2021)

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems. - Xiaokang Zhou; Wei Liang 006; Shohei Shimizu; Jianhua Ma 002; Qun Jin (IEEE Trans. Ind. Informatics, 2021)

A Multivariate Causal Discovery based on Post-Nonlinear Model. - Kento Uemura; Takuya Takagi; Kambayashi Takayuki; Hiroyuki Yoshida; Shohei Shimizu (CLeaR, 2022)

Causal Discovery for Linear Mixed Data. - Yan Zeng 002; Shohei Shimizu; Hidetoshi Matsui; Fuchun Sun 001 (CLeaR, 2022)

CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry. - Kazuhi Honjo; Xiaokang Zhou; Shohei Shimizu (IJCNN, 2022)

Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders. - Takashi Nicholas Maeda; Shohei Shimizu (Int. J. Data Sci. Anal., 2022)

Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System. - Xiaokang Zhou; Wei Liang 006; Weimin Li 001; Ke Yan 001; Shohei Shimizu; Kevin I-Kai Wang (IEEE Internet Things J., 2022)

A Survey on Integrity Auditing for Data Storage in the Cloud: From Single Copy to Multiple Replicas. - Angtai Li; Yu Chen 008; Zheng Yan 002; Xiaokang Zhou; Shohei Shimizu (IEEE Trans. Big Data, 2022)

B4SDC: A Blockchain System for Security Data Collection in MANETs. - Gao Liu; Huidong Dong; Zheng Yan 002; Xiaokang Zhou; Shohei Shimizu (IEEE Trans. Big Data, 2022)

Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems. - Xiaokang Zhou; Xuesong Xu; Wei Liang 006; Zhi Zeng; Shohei Shimizu; Laurence T. Yang; Qun Jin (IEEE Trans. Ind. Informatics, 2022)

Prospects of Continual Causality for Industrial Applications. - Daigo Fujiwara; Kazuki Koyama; Keisuke Kiritoshi; Tomomi Okawachi; Tomonori Izumitani; Shohei Shimizu (AAAI Bridge Program, 2023)

Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States. - Yi Jiang; Shohei Shimizu (CAWS, 2023)

Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise. - Genta Kikuchi; Shohei Shimizu (CAWS, 2023)

Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. - Daigo Fujiwara; Kazuki Koyama; Keisuke Kiritoshi; Tomomi Okawachi; Tomonori Izumitani; Shohei Shimizu (CLeaR, 2023)

Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. - Thuc Duy Le; Jiuyong Li; Robert Osazuwa Ness; Sofia Triantafillou; Shohei Shimizu; Peng Cui 001; Kun Kuang; Jian Pei 001; Fei Wang 001; Mattia Prosperi (CDPD, 2023)

BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis. - Shusuke Wani; Xiaokang Zhou; Shohei Shimizu (TrustCom, 2023)

Python package for causal discovery based on LiNGAM. - Takashi Ikeuchi; Mayumi Ide; Yan Zeng; Takashi Nicholas Maeda; Shohei Shimizu (J. Mach. Learn. Res., 2023)

Nonlinear Causal Discovery for High-Dimensional Deterministic Data. - Yan Zeng 002; Zhifeng Hao; Ruichu Cai; Feng Xie 002; Libo Huang; Shohei Shimizu (IEEE Trans. Neural Networks Learn. Syst., 2023)

Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications. - Xiaokang Zhou; Xiaozhou Ye; Kevin I-Kai Wang; Wei Liang 006; Nirmal-Kumar C. Nair; Shohei Shimizu; Zheng Yan 002; Qun Jin (IEEE Trans. Comput. Soc. Syst., 2023)

Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks. - Xiaokang Zhou; Xuzhe Zheng; Xuesong Cui; Jiashuai Shi; Wei Liang 006; Zheng Yan 002; Laurence T. Yang; Shohei Shimizu; Kevin I-Kai Wang (IEEE J. Sel. Areas Commun., 2023)

Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. - Thong Pham; Shohei Shimizu; Hideitsu Hino; Tam Le (CLeaR, 2024)

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. - Daisuke Takahashi; Shohei Shimizu; Takuma Tanaka (IJCNN, 2024)

Multi-Domain and Multi-View Oriented Deep Neural Network for Sentiment Analysis in Large Language Models. - Keito Inoshita; Xiaokang Zhou; Shohei Shimizu (iThings/GreenCom/CPSCom/SmartData/Cybermatics, 2024)

Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data. - Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2024)

Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis. - Hiroshi Yokoyama; Ryusei Shingaki; Kaneharu Nishino; Shohei Shimizu; Thong Pham (CoRR, 2024)

Causal-learn: Causal Discovery in Python. - Yujia Zheng 001; Biwei Huang; Wei Chen 103; Joseph D. Ramsey; Mingming Gong; Ruichu Cai; Shohei Shimizu; Peter Spirtes; Kun Zhang 001 (J. Mach. Learn. Res., 2024)

Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins. - Xin Liu 050; Rui Zhou 005; Shohei Shimizu; Rui Chong; Qingguo Zhou; Xiaokang Zhou (Digit. Commun. Networks, 2025)

Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding. - Yoshimitsu Morinishi; Shohei Shimizu (Trans. Mach. Learn. Res., 2025)

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. - Masayuki Takayama; Tadahisa Okuda; Thong Pham; Tatsuyoshi Ikenoue; Shingo Fukuma; Shohei Shimizu; Akiyoshi Sannai (Trans. Mach. Learn. Res., 2025)

Causal Additive Models with Unobserved Causal Paths and Backdoor Paths. - Thong Pham; Takashi Nicholas Maeda; Shohei Shimizu (CoRR, 2025)

Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization. - Xiaokang Zhou; Xuzhe Zheng; Tian Shu; Wei Liang 006; Kevin I-Kai Wang; Lianyong Qi; Shohei Shimizu; Qun Jin (IEEE Trans. Neural Networks Learn. Syst., 2025)

Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data. - Takashi Nicholas Maeda; Shohei Shimizu; Hidetoshi Matsui (CoRR, 2025)

Causal models and prediction in cell line perturbation experiments. - James P. Long; Yumeng Yang; Shohei Shimizu; Thong Pham; Kim-Anh Do (BMC Bioinform., 2025)