Causal Discovery Lab
Causal Discovery | 因果探索
Our group works on developing mathematical methods to elucidate the causal mechanisms underlying natural phenomena and human behavior. In particular, we develop statistical methods for estimating causal relationships from observational data that are obtained from sources other than randomized experiments and construct a new methodology that goes beyond the conventional limits. We also aim to contribute to the solution of problems in basic sciences such as natural and social sciences and applied sciences such as engineering and medicine by collaborating with people in those fields.
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を行います。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。
Members |メンバー
Shiga Univ. | 滋賀大学
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 and Undergraduate Students | 博士前期課程学生・学部生
10 master's students | 博士前期課程学生 10名
4 undergraduates | 学部生 4名
Visitors | ビジター
Hongjin Ren | 任 泓锦
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
RIKEN | 理研
News & Topics
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, 2024Causal-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, 2024Counterfactual 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, CLeaR2024Causal 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, 2024Structure 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, CLeaR2023Prospects 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, ICONIP2022Python package for causal discovery based on LiNGAM
T. Ikeuchi, M. Ide, Y. Zeng, T. N. Maeda, S. Shimizu, JMLR Machine Learning Open Source Software, 2023Statistical Causal Discovery: LiNGAM Approach
S. Shimizu, SpringerBriefs in Statistics, 2022CNN-GRU based deep learning model for demand forecast in retail industry
K. Honjo, X. Zhou, S. Shimizu, IJCNN2022Causal Discovery for Linear Mixed Data
Y. Zeng, S. Shimizu, H. Matsui, F. Sun, CLeaR2022
Python codeA Multivariate Causal Discovery based on Post-Nonlinear Model
K. Uemura, T. Takagi, T. Kambayashi, H. Yoshida, S. Shimizu, CLeaR2022
Python codeInternational 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 codeCausal 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 availableNeurIPS 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