Research Summary

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).

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