Research Summary

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)

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