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
KIKUCHI Genta | 菊池 元太
SAKAMOTO Yuji | 坂本 雄司
TAGAWA Kentaro | 田川 健太郎
JIANG Yi | 姜 益
Master's and Undergraduate Students | 博士前期課程学生・学部生
9 master's students | 博士前期課程学生 9名
5 undergraduates | 学部生 5名
Collaborators on JST CREST Causal Discovery Project | JST CREST 因果探索プロジェクト 協力者
RIKEN | 理研
News & Topics
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, Proc. 2nd Conference on Causal Learning and Reasoning, PMLR 213: 880-894, 2023.Prospects of Continual Causality for Industrial Applications
D. Fujiwara, K. Koyama, K. Kiritoshi, T. Okawachi, T. Izumitani, S. Shimizu, Proc. 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
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