| 2026 | AAAI | Discovering Linear Non-Gaussian Models for All Categories of Missing Data (Student Abstract). | Matteo Ceriscioli, Shohei Shimizu, Karthika Mohan |
| 2026 | AAAI | I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables. | Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi, Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu |
| 2025 | IJCNN | 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 |
| 2024 | IJCNN | Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating. | Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka |
| 2023 | AAAI | Prospects of Continual Causality for Industrial Applications. | Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu |
| 2023 | TrustCom | BiLSTM and VAE Enhanced Multi-Task Neural Network for Trust-Aware E-Commerce Product Analysis. | Shusuke Wani, Xiaokang Zhou, Shohei Shimizu |
| 2022 | IJCNN | CNN-GRU Based Deep Learning Model for Demand Forecast in Retail Industry. | Kazuhi Honjo, Xiaokang Zhou, Shohei Shimizu |
| 2021 | IJCAI | Causal Discovery with Multi-Domain LiNGAM for Latent Factors. | Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao |
| 2021 | KDD | 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 |
| 2021 | UAI | Causal additive models with unobserved variables. | Takashi Nicholas Maeda, Shohei Shimizu |
| 2020 | AISTATS | RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. | Takashi Nicholas Maeda, Shohei Shimizu |
| 2020 | ICASSP | Estimation of Post-Nonlinear Causal Models Using Autoencoding Structure. | Kento Uemura, Shohei Shimizu |
| 2018 | AISTATS | Cause-Effect Inference by Comparing Regression Errors. | Patrick Blbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schlkopf |
| 2018 | DASC | A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree. | Weimin Li, Heng Zhu, Xiaokang Zhou, Shohei Shimizu, Mingjun Xin, Qun Jin |
| 2017 | ESANN | A novel principle for causal inference in data with small error variance. | Patrick Blbaum, Shohei Shimizu, Takashi Washio |
| 2012 | ICANN | Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. | Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvrinen, Takashi Washio |
| 2012 | ICDM | Bootstrap Confidence Intervals in DirectLiNGAM. | Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, Tatsuya Tashiro |
| 2011 | UAI | Discovering causal structures in binary exclusive-or skew acyclic models. | Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara |
| 2010 | ICANN | Assessing Statistical Reliability of LiNGAM via Multiscale Bootstrap. | Yusuke Komatsu, Shohei Shimizu, Hidetoshi Shimodaira |
| 2010 | ICANN | Discovery of Exogenous Variables in Data with More Variables Than Observations. | Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvrinen, Takashi Washio, Teppei Shimamura, Seiya Imoto |
| 2010 | IJCNN | An experimental comparison of linear non-Gaussian causal discovery methods and their variants. | Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio |
| 2009 | UAI | A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. | Shohei Shimizu, Aapo Hyvrinen, Yoshinobu Kawahara |
| 2008 | ICML | Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. | Aapo Hyvrinen, Shohei Shimizu, Patrik O. Hoyer |
| 2008 | UAI | Causal discovery of linear acyclic models with arbitrary distributions. | Patrik O. Hoyer, Aapo Hyvrinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu |
| 2007 | ICONIP | Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes. | Shohei Shimizu, Aapo Hyvrinen |
| 2006 | ICANN | A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis. | Aapo Hyvrinen, Shohei Shimizu |
| 2005 | UAI | Discovery of Non-gaussian Linear Causal Models using ICA. | Shohei Shimizu, Aapo Hyvrinen, Yutaka Kano, Patrik O. Hoyer |