Publications
Causality and prediction
NEW 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.]
Causal discovery: LiNGAM
LiNGAM homepage
Links to LiNGAM-related papers
Reviews and tutorials
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
C. Li and S. Shimizu. Combining linear non-Gaussian acyclic model with logistic regression model for estimating causal structure from mixed continuous and discrete data. Arxiv preprint arXiv:1802.05889, 2018.
[pdf] [Google scholar]
[Proposes an approach for inferring causal structure from mixed continuous and discrete data combining LiNGAM and logistic regression 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. This is based on ICML2008 paper.]K. Kadowaki, S. Shimizu, and T. Washio. Estimation of causal structures in longitudinal data using non-Gaussianity. In Proc. 23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP2013), pp. 1--6, Southampton, United Kingdom, 2013.
[pdf] [Python code] [Google scholar]
[Considers learning causal structures in longitudinal data that collects multiple samples over a period of time.]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] [erratum] [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. This is based on UAI2005 paper.]
Hidden variable models
NEW 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 xx-xx, Montreal-themed Virtual Reality, 2021.
[pdf] [Google scholar]
[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]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. This is based on ICANN2012 paper.]S. Shimizu and K. Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15: 2629-2652, 2014.
[pdf] [Google scholar]
A short introduction
Python code
[Considers the problem of estimating the causal direction of two observed variables in the presence of hidden common causes. Develops a new approach based on a linear non-Gaussian acyclic structural equation model (LiNGAM) and a linear mixed model. The new approach does not require to specify the number of hidden common causes.]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). This is based on PGM2006 paper.]
Nonlinearity
NEW T. N. Maeda and S. Shimizu. Causal additive models with unobserved variables. In Proc. 37th Conf. on Uncertainty in Artificial Intelligence (UAI2021), pages xx–xx, Online, 2021.
[pdf] [Google scholar]K. Uemura and S. Shimizu. Estimation of post-nonlinear causal models using autoencoding structure. In Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), pages 3312-3316, Barcelona, Spain, 2020.
[pdf] [Google scholar]
[Proposes an estimation method for post-nonlinear causal model using an autoenconding structure.]
Statistical reliability
K. Thamvitayakul, S. Shimizu, T. Ueno, T. Washio and T. Tashiro. Bootstrap confidence intervals in DirectLiNGAM. In Proc. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW2012), pp.659--668, Brussels, Belgium, 2012.
[pdf] [erratum] [Google scholar]
[Considers to compute Bootstrap confidence intervals in LiNGAM.]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.
P. Blöbaum, D. Janzing, T. Washio, S. Shimizu, B. Schölkopf. Analysis of cause-effect inference by comparing regression errors. PeerJ Computer Science 5:e169, 2019.
[pdf] [Google scholar]
[Proposes a nonlinear method for estimating the causal direction of two variables comparing the least-squares errors. This is based on AISTATS2018 paper. ]R. Silva and S. Shimizu. Learning instrumental variables with structural and non-Gaussianity assumptions. Journal of Machine Learning Research, 18: 1--49, 2017.
[pdf] [Google scholar]
[Proposes a method for learning instrumental variables based on non-Gaussianity.]