Our research group in Data Science and AI develops mathematical methodologies to elucidate the causal mechanisms underlying natural phenomena and human behavior. In particular, we focus on creating statistical methods for estimating causal relationships from observational data obtained outside the context of randomized experiments, aiming to establish a new methodological framework that transcends conventional limitations. Additionally, through collaboration with researchers in a broad range of disciplines, we strive to contribute to solving problems in both basic sciences—such as the natural and social sciences—and applied fields including engineering and medicine.
データサイエンス・AIの研究室です。特徴は、自然現象や人間行動の根底にある因果メカニズムを解明するための数理的方法論に関する研究です。特に、介入のない観察データから因果関係を推測するための数学的方法論を研究開発し、従来の限界を超える新しい方法論体系を構築します。また、 様々な科学分野の研究者と協力して自然科学・社会科学などの基礎科学や工学・医学などの応用科学の問題にも取り組み、方法論の立場から問題の解決に貢献することを目指します。
Professors | 教員
Administrative assistants | 事務補佐員
OKADA Hiroko | 岡田 拡子
Master's and Undergraduate Students | 博士前期課程学生・学部生
7 master's students | 博士前期課程学生 7名
4 undergraduates | 学部生 4名
Former staff | 旧スタッフ
Professors | 教員
Ph.D. Students | 博士後期課程学生
SAKAMOTO Yuji | 坂本 雄司
TAGAWA Kentaro | 田川 健太郎
MORINISHI Yoshimitsu | 森西 美光
Master's Students | 博士前期課程学生
4 master's students | 博士前期課程学生 4名
Collaborators on JST CREST Causal Discovery Project | JST CREST 因果探索プロジェクト 協力者
Commissioned researchers | 受託研究員
Integrating large language models in causal discovery: a statistical causal approach
M. Takayama, T. Okuda, T. Pham, T. Ikenoue, S. Fukuma, S. Shimizu, A. Sannai, Arxiv preprint, 2024. Accepted in TMLR
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
H. Yokoyama, R. Shingaki, K. Nishino, S. Shimizu, and T. Pham, Arxiv preprint, 2024. Accepted in Proc. IJCNN2025
Causal models and prediction in cell line perturbation experiments
J. Long, Y. Yang, S. Shimizu, T. Pham, and K.-A. Do, BMC Bioinformatics, 2025
ゼミ生の辻さん、武田さん、畑中さんがNEC Analytics Challenge Cup 2024 データ活用コンテストで優秀賞
Causal discovery with hidden variables based on non-Gaussianity and nonlinearity
T. N. Maeda, Y. Zeng, and S. Shimizu, Dependent Data in Social Sciences Research, 2024
Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data
T. N. Maeda and S. Shimizu, Behaviormetrika, 2024
6th Pacific Causal Inference Conference (PCIC 2024), Shanghai, China
Causal-learn: Causal discovery in Python
Y. Zheng, B. Huang, W. Chen, J. Ramsey, M. Gong, R. Cai, S. Shimizu, P. Spirtes, K. Zhang, JMLR Machine Learning Open Source Software, 2024
Counterfactual explanations of black-box machine learning models using causal discovery with applications to credit rating
D. Takahashi, S. Shimizu, and T. Tanaka, IJCNN2024. Accepted.
Scalable counterfactual distribution estimation in multivariate causal models
T. Pham, S. Shimizu, H. Hino, T. Le, CLeaR2024
Causal discovery with hidden variables based on non-Gaussianity and non-linearity
T. N. Maeda, Y. Zeng, and S. Shimizu. In Dependent Data in Social Sciences Research (2nd edition), Springer, 2024
Structure Learning for Groups of Variables in Nonlinear Time-Series Data with Location-Scale Noise
G. Kikuchi and S. Shimizu, Causal Analysis Workshop 2023 (CAWS2023)
Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States
Y. Jiang and S. Shimizu, Causal Analysis Workshop 2023 (CAWS2023)
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling
D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, S. Shimizu, CLeaR2023
Prospects of Continual Causality for Industrial Applications
D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, S. Shimizu, First AAAI Bridge Program on Continual Causality, 2023
Differentiable causal discovery under heteroscedastic noise
G. Kikuchi, ICONIP2022
Python package for causal discovery based on LiNGAM
T. Ikeuchi, M. Ide, Y. Zeng, T. N. Maeda, S. Shimizu, JMLR Machine Learning Open Source Software, 2023
Statistical Causal Discovery: LiNGAM Approach
S. Shimizu, SpringerBriefs in Statistics, 2022
CNN-GRU based deep learning model for demand forecast in retail industry
K. Honjo, X. Zhou, S. Shimizu, IJCNN2022
Causal Discovery for Linear Mixed Data
Y. Zeng, S. Shimizu, H. Matsui, F. Sun, CLeaR2022
Python code
A Multivariate Causal Discovery based on Post-Nonlinear Model
K. Uemura, T. Takagi, T. Kambayashi, H. Yoshida, S. Shimizu, CLeaR2022
Python code
International Workshop on Causality and Philosophy, Online
4th March 2022 16:00-18:00 JST
15:00-17:00 in Hong Kong
8:00-10:00 in Berlin
統計的因果探索: 領域知識とデータから因果仮説を探索する
2021年度 JST-理研 合同AIP公開シンポジウム
International Symposium on Causal Inference and Machine Learning September 10-11, 2021
The KDD2021 Workshop on Causal Discovery (CD2021)
Video (Preliminary Version)
Estimating individual-level optimal causal interventions combining causal models and machine learning models
K. Kiritoshi, T. Izumitani, K. Koyama, T. Okawachi, K. Asahara, and S. Shimizu
Proc. 2021 ACM SIGKDD Workshop on Causal Discovery (CD2021)
Causal additive models with unobserved variables
T. N. Maeda and S. Shimizu, UAI2021
Python code
Causal discovery with multi-domain LiNGAM for latent factors
Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto, and Z. Hao, IJCAI2021
Python code
[27th AIP Open Seminar] Talks by Causal Inference Team on 2nd June, 2021
The recording available
NeurIPS 2020 Workshop on Causal Discovery and Causality-Inspired Machine Learning
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders
T. N. Maeda and S. Shimizu, AISTATS2020
Python code