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RIKEN Center for Advanced Intelligence Project Causal Inference Team

Team Leader: Shohei Shimizu (D.Eng.)

Research Summary

Shohei  Shimizu(D.Eng.)

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

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