Selected publications

Complete publication list
My publications on Google scholar

Causal discovery and machine learning

• NEW H. Yokoyama, R. Shingaki, K. Nishino, S. Shimizu, T. Pham. Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis. In 2025 International Joint Conference on Neural Networks (IJCNN2025), pages 1-10, Rome, Italy, 2025.
  [pdf] [Google scholar]

• M. Takayama, T. Okuda, T. Pham, T. Ikenoue, S. Fukuma, S. Shimizu, A. Sannai. Integrating large language models in causal discovery: a statistical causal approach. Transactions on Machine Learning Research (TMLR), 2025.
  [openreview] [Google scholar]

• D. Takahashi, S. Shimizu, and T. Tanaka. Counterfactual explanations of black-box machine learning models using causal discovery with applications to credit rating. In Proc. Int. Joint Conf. on Neural Networks (IJCNN2024), part of the 2024 IEEE World Congress on Computational Intelligence (WCCI2024), pages 1--8, Yokohama, Japan, 2024.
  [pdf] [Google scholar]

• K. Kiritoshi, T. Izumitani, K. Koyama, T. Okawachi, K. Asahara, and S. Shimizu. Estimating individual-level optimal causal interventions combining causal models and machine learning models. In Proc. KDD'21 Workshop on Causal Discovery, PMLR 150:55-77, 2021.
  [pdf] [Google scholar]
  [Proposes a method for estimating individual-level optimal causal intervention by combining causal discovery and machine learning.]

• P. Blöbaum and S. Shimizu. Estimation of interventional effects of features on prediction. In Proc. 2017 IEEE International Workshop on Machine Learning for Signal Processing (MLSP2017), pp. 1--6, Tokyo, Japan, 2017.
  [pdf] [Google scholar]
  A short introduction
  Python code
  [Proposes a new framework to understand the prediction mechanisms of predictive models based on causality.]

Case studies of causal discovery

• T. Okuda, S. Shimizu, T. Pham, T. Ikenoue, S. Fukuma. Operationalizing longitudinal causal discovery under real-world workflow constraints. arXiv:2602.23800, 2026.
  [pdf] [Google scholar]

• Y. Jiang, S. Shimizu, and M. Yamamoto. Demographic influences on investment: a causal discovery study in Japan. Array, 2026.
  [pdf] [Google scholar]

• K. Hiki, T. Pham, M. Yamamoto,  T. I. Hayashi, S. Shimizu. Statistical causal discovery in developing and refining adverse outcome pathway. Environmental Toxicology and Chemistry, vgag067, 2026.
  [pdf] [code] [Google scholar]

• Y. Jiang and S. Shimizu. Financial literacy may not directly drive investment participation or retirement planning in Japan, Frontiers in Behavioral Economics, 4: 1725333, 2026.
  [pdf] [Google scholar]

• Y. Jiang and S. Shimizu. Linkages among the foreign exchange, stock, and bond markets in Japan and the United States. In Proc. Causal Analysis Workshop 2023 (CAWS2023), PMLR 223:1-19, 2023.
  [pdf] [Google scholar]

• 高山正行・清水昌平. 科学技術・イノベーション政策の立案・評価への統計的因果探索の応用.  研究 技術 計画 (特集 科学技術・イノベーション政策科学の学際的アプローチ：より良いアジェンダ・セッティングのために), 40(3-4): 341-355, 2025.
  [pdf] [Google scholar]

Causal discovery software

• Y. Zheng, B. Huang, W. Chen, J. Ramsey, M. Gong, R. Cai, S. Shimizu, P. Spirtes, K. Zhang. Causal-learn: Causal discovery in Python.  Journal of Machine Learning Research, 25(60):1−8, 2024.
  [A Python package for performing causal discovery methods including conditonal-independence-based methods and LiNGAM methods.]
  [pdf] [github] [Google scholar]

• T. Ikeuchi, M. Ide, Y. Zeng, T. N. Maeda, and S. Shimizu. Python package for causal discovery based on LiNGAM. Journal of Machine Learning Research, 24(14):1−8, 2023.
  [pdf] [Google scholar]
  Python package
  [A Python package for performing causal discovery analysis based on LiNGAM approach.]
  [LiNGAM Python package: Tutorial slides]
  [LiNGAM Pythonパッケージでできること: 紹介スライド]

Causal discovery

LiNGAM homepage
Links to LiNGAM-related papers

Reviews and tutorials

• 三内・高山 ・清水. 大規模言語モデルを活用した統計的因果探索の新展開 ― 統計的因果プロンプティングによる専門知識とデータの融合 ―. SBI金融経済研究所所報 vol.8, pages 46–61, 2025.
  [pdf] [Google scholar] 

• T. N. Maeda, Y. Zeng, and S. Shimizu. Causal discovery with hidden variables based on non-Gaussianity and non-linearity. In Dependent Data in Social Sciecnes Research - Forms, Issues and Methods of Analysis (2nd edition), pages 181–205. Springer, 2024.
  [pdf] [Google scholar] 

• S. Shimizu. Statistical causal discovery: LiNGAM approach. SpringerBriefs in Statistics. Springer Tokyo, 2022.
  [pdf] [Google scholar] 

• S. Shimizu and P. Blöbaum. Recent advances in semi-parametric methods for causal discovery. In Direction Dependence in Statistical Models: Methods of Analysis (W. Wiedermann, D. Kim, E. Sungur, and A. von Eye, eds.), pages xx–xx. Wiley, 2020.
  [pdf] [Google scholar] 

• S. Shimizu.  LiNGAM: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 41(1): 65--98, 2014.
  [pdf] [Google scholar]

• S. Shimizu. Non-Gaussian Methods for Learning Linear Structural Equation Models: Part I. The 26th Conference on Uncertainty in Artificial Intelligence (UAI2010), Catalina Island, California, USA , 2010. Tutorial
  [slides] [references]

Basic models with no hidden variables

• NEW  H. Yokoyama,  R. Takeuchi, S. Shimizu. Causal Discovery of Synchronous Neural Oscillations based on Jacobian-informed VAR-LiNGAM. bioRxiv 2026.04.28.721377, 2026.
  [pdf] [Google scholar]
  [Propose j-VAR-LiNGAM, an extended LiNGAM method for estimating causal structure—particularly hierarchical structure—from synchronous time series of neural dynamics.]

• T. N. Maeda, S. Shimizu, H. Matsui. Density ratio-based causal discovery from bivariate continuous-discrete data. Arxiv preprint arXiv: 2501.12854, 2025.
  [pdf] [Google scholar]
  [Proposes a causal discovery method for mixed bivariate data consisting of one continuous and one discrete variable under rather mild conditions.]

• Y. Zeng, S. Shimizu, H. Matsui, F. Sun. Causal discovery for linear mixed data. In Proc. First Conference on Causal Learning and Reasoning (CLeaR2022), PMLR 177, pages 994-1009, 2022.
  [pdf] [Google scholar]
  Python code
  [Proposes an approach for inferring causal structure from mixed continuous and discrete data combining LiNGAM and logistic-type model.]

• A. Hyvärinen, K. Zhang, S. Shimizu, and P. O. Hoyer. Estimation of a structural vector autoregression model using non-Gaussianity. Journal of Machine Learning Research, 11: 1709−1731, 2010.
  [pdf] [Google scholar]
  Videolecture
  R code by Doris Entner
  Matlab code by Luca Faes
  Python code
  [Shows how LiNGAM and autoregressive models are combined to estimate a structural vector autoregression model for time series data.]

• S. Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012.
  [pdf] [Google scholar]
  Python code
  [Proposes a framework to perform LiNGAM analysis on heterogenous datasets.]

• S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer and K. Bollen. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12(Apr): 1225--1248, 2011.
  [pdf] [Google scholar]
  Python code
  R code by Genta Kikuchi
  [Proposes a new estimation algorithm for LiNGAM. The new estimation method called DirectLiNGAM requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model, i.e., if all the model assumptions are met and the sample size is infinite.]

• S. Shimizu, P. O. Hoyer, A. Hyvärinen and A. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7: 2003--2030, 2006.
  [pdf] [Matlab/Octave code] [Google scholar]
  A short introduction
  R code by Patrik O. Hoyer and Antti Hyttinen
  R code by Doris Entner
  R package: pcalg by Kalisch et al.
  Python code
  TETRAD IV
  [Proposes a novel identifiable model (LiNGAM) for causal discovery and an ICA-based estimation algorithm to learn the model. Original article introducing LiNGAM.]

Hidden variable models

• Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto, Z. Hao. Causal discovery with multi-domain LiNGAM for latent factors. In Proc. the 30th International Joint Conference on Artificial Intelligence (IJCAI2021), pages 2097-2103, Montreal-themed Virtual Reality, 2021.
  [pdf] [Google scholar]
  Python code
  [Considers to estimate LiNGAM model for latent factors  from multi-domain data.]

• T. N. Maeda and S. Shimizu. RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. In JMLR Workshop and Conference Proceedings, AISTATS2020 (Proc. 23rd International Conference on Artificial Intelligence and Statistics), pages 735–745, Palermo, Sicily, Italy, 2020.
  [pdf] [Google scholar]
  Python code
  [Proposes an extension of the LiNGAM model that allows hidden common causes.]

• T. Tashiro, S. Shimizu, A. Hyvärinen and T. Washio. ParceLiNGAM: A causal ordering method robust against latent confounders. Neural Computation, 26(1): 57--83, 2014.
  [pdf] [Google scholar]
  code
  [Develops a variant of DirectLiNGAM that is robust against hidden common causes. ]

• Y. Kawahara, K. Bollen, S. Shimizu and T. Washio. GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables. Arxiv preprint arXiv:1006.5041, 2010.
  [pdf] [Google scholar]

• P. O. Hoyer, S. Shimizu, A. Kerminen, and M. Palviainen. Estimation of causal effects using linear non-gaussian causal models with hidden variables. International Journal of Approximate Reasoning, 49(2): 362-378, 2008.
  [pdf] (7.0MB) [doi] [Matlab code] [Google scholar]
  [Proposes an extension of basic LiNGAM above to cases with latent common cause variables. The new model is called Latent variable LiNGAM (LvLiNGAM). ]

Nonlinearity

• NEW T. Pham, T. N. Maeda, and S. Shimizu. Causal Additive Models with Unobserved Causal Paths and Backdoor Paths. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS2026), PMLR xx: xx-xx, 2026. 
  [pdf] [Google scholar]
  [Establish sufficient conditions under which causal directions can be identified and provides a sound and complete algorithm.]

• T. N. Maeda and S. Shimizu. Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data. Behaviormetrika, 52: 529-547, 2025.
  [pdf] [Google scholar]
  [Extends that CAM-UV so that it can incorporate prior knowlede and handle time series data.]

• G. Kikuchi and S. Shimizu. Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise. In Proc. Causal Analysis Workshop 2023 (CAWS2023), PMLR 223:20-39, 2023.
  [pdf] [code] [Google scholar]
  [Proposes a structure-learning algorithm for nonlinear time-series data with location-scale noise.]

• D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, and S. Shimizu. Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. In Proc. 2nd Conference on Causal Learning and Reasoning, PMLR 213: 880-894, 2023.
  [pdf] [Google scholar]
  [Proposes a causal discovery method JIT-LiNGAM, based on the Linear Non-Gaussian Acyclic Model (LiNGAM) and the Just-In-Time (JIT) framework to approximate an inherently globally non-linear model with local linear models.]

• K. Uemura, T. Takagi, T. Kambayashi, H. Yoshida, and S. Shimizu. A Multivariate Causal Discovery based on Post-Nonlinear Model. In Proc. First Conference on Causal Learning and Reasoning (CLeaR2022), PMLR 177, pages 826-839, 2022.
  [pdf] [Google scholar]
  Python code
  [Proposes a multivariate estimation method for post-nonlinear causal model using an autoenconding structure.]

• T. N. Maeda and S. Shimizu. Causal additive models with unobserved variables. In Proc. 37th Conf. on Uncertainty in Artificial Intelligence (UAI2021), PMLR 161:97-106, 2021.
  [pdf] [Google scholar]
  Python code
  Python code in causal-learn
  [Proposes an additive nonlinear model that allows unobserved common causes and unobserved intermediate variables.]

Statistical reliability

• Y. Komatsu, S. Shimizu, and H. Shimodaira. Assessing statistical reliability of LiNGAM via multiscale bootstrap. In Proc. 20th International Conference on Artificial Neural Networks (ICANN2010), pp.309--314, Thessaloniki, Greece, 2010.
  [pdf] [doi] [Google scholar]
  Related code: R code for multiscale bootstrap
  [Proposes a method to evaluate statistical reliability of causal orderings estimated by LiNGAM.]

Model fit

• S. Shimizu and Y. Kano. Use of non-normality in structural equation modeling: Application to direction of causation. Journal of Statistical Planning and Inference, 138: 3483--3491, 2008.
  [pdf] [Google scholar]
  [Proposes a test statistics to evaluate model fit using higher-order moments.]

Causal inference: misc.

• D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, and S. Shimizu. Prospects of continual causality for industrial applications. In Proc. First AAAI Bridge Program on Continual Causality, PMLR 208:18-24, 2023.
  [pdf] [Google scholar]