Shimizu, Shohei

22 publications

TMLR 2025 Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding Yoshimitsu Morinishi, Shohei Shimizu

TMLR 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

MLOSS 2024 Causal-Learn: Causal Discovery in Python Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

CLeaR 2024 Scalable Counterfactual Distribution Estimation in Multivariate Causal Models Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le

CLeaR 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

MLOSS 2023 Python Package for Causal Discovery Based on LiNGAM Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu

CLeaR 2022 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, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun

UAI 2021 Causal Additive Models with Unobserved Variables Takashi Nicholas Maeda, Shohei Shimizu

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

AISTATS 2020 RCD: Repetitive Causal Discovery of Linear Non-Gaussian Acyclic Models with Latent Confounders Takashi Nicholas Maeda, Shohei Shimizu

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

JMLR 2017 Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions Ricardo Silva, Shohei Shimizu

JMLR 2014 Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-Specific Confounder Variables and Non-Gaussian Distributions Shohei Shimizu, Kenneth Bollen

JMLR 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

UAI 2011 Discovering Causal Structures in Binary Exclusive-or Skew Acyclic Models Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

JMLR 2010 Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer

UAI 2009 A Direct Method for Estimating a Causal Ordering in a Linear Non-Gaussian Acyclic Model Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara

UAI 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

ICML 2008 Causal Modelling Combining Instantaneous and Lagged Effects: An Identifiable Model Based on Non-Gaussianity Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer

JMLR 2006 A Linear Non-Gaussian Acyclic Model for Causal Discovery Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen

UAI 2005 Discovery of Non-Gaussian Linear Causal Models Using ICA Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer