RIKEN Center for Advanced Intelligence Project Causal Inference Team
Team Leader: Shohei Shimizu (D.Eng.)
Research Summary

Our group works on different topics related to causal inference. In particular, we develop theory, methods, algorithms, and software for estimating causal relations based on data that are obtained from sources other than randomized experiments, i.e., causal discovery.
Research Subjects:
- Causal discovery
Main Research Fields
- Informatics
Related Research Fields
- Engineering
- Social Sciences
- Statistical Science
Selected Publications
- 1.
Pham, T., Shimizu, S., Hino, H., Le, T.:
"Scalable counterfactual distribution estimation in multivariate causal models"
Proc. Third Conference on Causal Learning and Reasoning (CLeaR2024), pp. 1118-1140 - 2.
Maeda, T. N., Shimizu, S.:
"Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data"
Behaviormetrika, (2024) - 3.
Ikeuchi, T., Ide, M., Zeng, Y., Maeda, T. N., Shimizu, S.:
"Python package for causal discovery based on LiNGAM"
Journal of Machine Learning Research , 24, 1--8 (2023) - 4.
Shimizu, S.:
"Statistical Causal Discovery: LiNGAM Approach"
Springer, Tokyo (2022) - 5.
Uemura, K., Takagi, T., Kambayashi, T., Yoshida, Y., Shimizu, S.:
"A multivariate causal discovery based on post-nonlinear model"
Proc. First Conference on Causal Learning and Reasoning (CLeaR2022), pp. 826-839 (2022) - 6.
Zeng, Y., Shimizu, S., Matsui, H., Sun, F.:
"Causal discovery for linear mixed data"
Proc. First Conference on Causal Learning and Reasoning (CLeaR2022), pp. 994-1009. (2022) - 7.
Maeda, T. N., Shimizu, S.:
"Causal Additive Models with Unobserved Variables
Proc. 37th Conference on Uncertainty in Artificial" Intelligence (UAI2021), pp. 97-106 (2021) - 8.
Maeda, T. N. and Shimizu, S.:
"RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), pp. 735-745. (2020) - 9.
Blöbaum, P. and Shimizu, S.:
"Estimation of interventional effects of features on prediction"
Proc. 2017 IEEE Machine Learning for Signal Processing Workshop (MLSP2017), pp. 1-6. (2017) - 10.
*Shimizu, S., Hoyer, P. O., Hyvärinen, A., and Kerminen, A.:
"A linear non-gaussian acyclic model for causal discovery"
Journal of Machine Learning Research, 7, 2003--2030 (2006)
Related Links
Lab Members
Principal investigator
- Shohei Shimizu
- Team Leader
Core members
- Takashi Nicholas Maeda
- Visiting Scientist
- Jun Otsuka
- Visiting Scientist
- Thong Pham
- Visiting Scientist
- Hidetoshi Shimodaira
- Visiting Scientist
- Akifumi Okuno
- Visiting Scientist
- Yoshikazu Terada
- Visiting Scientist
- Hiroshi Yokoyama
- Visiting Scientist
- Yan Zeng
- Visiting Scientist
- Xiaokang Zhou
- Visiting Scientist
Contact Information
Shiga University,
1-1-1 Bamba,
Hikone, Shiga, 522-8522, Japan
Email: shohei.shimizu@riken.jp