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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

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

  • 1. 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).
  • 2. Uemura, K. and Shimizu, S.:
    "Estimation of post-nonlinear causal models using autoencoding structure"
    Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), pp. 3312-3316. (2020).
  • 3. Blöbaum, P. Janzing, D., Washio, T., Shimizu, S. and Schölkopf, B.:
    "A novel principle for causal inference in data with small error variance"
    Proc. 21st International Conference on Artificial Intelligence and Statistics (AISTATS2018), pp. 735-745. (2018).
  • 4. 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)
  • 5.*Shimizu, S., and Bollen, K.
    "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).
  • 6.*Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P. O., and Bollen, K.:
    "DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model"
    Journal of Machine Learning Research, 12, 1225--1248 (2011).
  • 7.*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

Chao Li
Postdoctoral Researcher
Takashi Nicholas Maeda
Postdoctoral Researcher

Careers

Position Deadline
Seeking a Senior Research Scientist or Research Scientist (20R55) Dec 8, 2020

Contact Information

Shiga University,
1-1-1 Bamba,
Hikone, Shiga, 522-8522, Japan
Email: shohei-shimizu [at] biwako.shiga-u.ac.jp

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