Maeda TakashiNicholas 前田高志ニコラス

… Alternative Names

Takashi Nisholas Maeda 前田高志ニコラス

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Researcher Number	20848361
Other IDs
Affiliation (Current)	2026: 学習院大学, 付置研究所, 准教授
Affiliation (based on the past Project Information) *help	2024: 学習院大学, 付置研究所, 准教授 2022 – 2023: 東京電機大学, システムデザイン工学部, 准教授 2020 – 2021: 国立研究開発法人理化学研究所, 革新知能統合研究センター, 特別研究員
Review Section/Research Field	Principal Investigator Basic Section 61030:Intelligent informatics-related
Keywords	Principal Investigator 因果推論 / 統計的因果探索 / 機械学習 / 統計的因果推論 / 未観測共通原因 / 未観測変数

Realization of causal model-based machine learning that autonomously responds to changePrincipal Investigator
- Principal Investigator
  
  前田高志ニコラス
- Project Period (FY)
  2023 – 2025
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 61030:Intelligent informatics-related
- Research Institution
  Gakushuin University
  Tokyo Denki University
Causal discovery from data in the presence of unobserved common causesPrincipal Investigator
- Principal Investigator
  
  Takashi Nisholas Maeda
- Project Period (FY)
  2020 – 2022
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 61030:Intelligent informatics-related
- Research Institution
  Tokyo Denki University
  Institute of Physical and Chemical Research

All 2024 2023 2022 2021 2020

All Journal Article Presentation

[Journal Article] Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data2024
- Author(s)
  Maeda Takashi Nicholas、Shimizu Shohei
- Journal Title
  
  Behaviormetrika
  
  Volume: - Issue: 2 Pages: 1-19
- DOI
  10.1007/s41237-024-00238-1
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K11708, KAKENHI-PROJECT-23K16951
[Journal Article] Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity2024
- Author(s)
  Maeda Takashi Nicholas、Zeng Yan、Shimizu Shohei
- Journal Title
  
  Dependent Data in Social Sciences Research
  
  Volume: なし Pages: 181-205
- DOI
  10.1007/978-3-031-56318-8_8
- ISBN
  9783031563171, 9783031563188
- Data Source
  KAKENHI-PROJECT-23K16951
[Journal Article] Python package for causal discovery based on LiNGAM2023
- Author(s)
  Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
- Journal Title
  
  Journal of Machine Learning Research
  
  Volume: 24 Pages: 1-8
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K19872
[Journal Article] Python package for causal discovery based on LiNGAM2023
- Author(s)
  Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
- Journal Title
  
  Journal of Machine Learning Research
  
  Volume: 24(14)
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-23K16951
[Journal Article] I-RCD: an improved algorithm of repetitive causal discovery from data with latent confounders2022
- Author(s)
  Maeda Takashi Nicholas
- Journal Title
  
  Behaviormetrika
  
  Volume: 49 Issue: 2 Pages: 329-341
- DOI
  10.1007/s41237-022-00160-4
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K19872
[Journal Article] Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders2021
- Author(s)
  Maeda Takashi Nicholas、Shimizu Shohei
- Journal Title
  
  International Journal of Data Science and Analytics
  
  Volume: 13 Issue: 2 Pages: 77-89
- DOI
  10.1007/s41060-021-00282-0
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K19872
[Journal Article] RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders2020
- Author(s)
  Takashi Nicholas Maeda, Shohei Shimizu
- Journal Title
  
  Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics
  
  Volume: 108 Pages: 735-745
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K19872
[Presentation] 統計的因果探索アルゴリズム”LiNGAM”を用いた若手研究者支援政策に関する研究2022
- Author(s)
  高山正行, 小柴等, 前田高志ニコラス, 三内顕義, 清水昌平, 星野利彦
- Organizer
  研究・イノベーション学会第37 回年次学術大会
- Data Source
  KAKENHI-PROJECT-20K19872
[Presentation] Causal discovery in the presence of unobserved variables2021
- Author(s)
  Takashi Nicholas MAEDA
- Organizer
  International Symposium on Causal Inference and Machine Learning
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K19872
[Presentation] 未観測共通原因が存在するときの因果グラフ推定2021
- Author(s)
  Takashi Nicholas MAEDA
- Organizer
  応用統計学会・日本計量生物学会
- Invited
- Data Source
  KAKENHI-PROJECT-20K19872
[Presentation] Causal additive models with unobserved variables2021
- Author(s)
  Takashi Nicholas MAEDA, Shohei SHIMIZU
- Organizer
  The 37th Conference on Uncertainty in Artificial Intelligence (UAI2021)
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K19872
[Presentation] 統計的因果探索入門2020
- Author(s)
  前田高志ニコラス
- Organizer
  第23回情報論的学習理論ワークショップ (IBIS2020)
- Invited
- Data Source
  KAKENHI-PROJECT-20K19872

1. SHIMIZU Shohei

# of Collaborated Projects: 0 results

# of Collaborated Products: 1 results

Maeda TakashiNicholas 前田 高志ニコラス

Takashi Nisholas Maeda 前田 高志ニコラス

Research Projects

Research Products

Co-Researchers

Realization of causal model-based machine learning that autonomously responds to changePrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Causal discovery from data in the presence of unobserved common causesPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Use of prior knowledge to discover causal additive models with unobserved variables and its application to time series data2024

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity2024

Author(s)

Journal Title

DOI

ISBN

Data Source

[Journal Article] Python package for causal discovery based on LiNGAM2023

Author(s)

Journal Title

Data Source

[Journal Article] Python package for causal discovery based on LiNGAM2023

Author(s)

Journal Title

Data Source

[Journal Article] I-RCD: an improved algorithm of repetitive causal discovery from data with latent confounders2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders2021

Author(s)

Journal Title

DOI

Data Source

[Journal Article] RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders2020

Author(s)

Journal Title

Data Source

[Presentation] 統計的因果探索アルゴリズム”LiNGAM”を用いた若手研究者支援政策に関する研究2022

Author(s)

Organizer

Data Source

[Presentation] Causal discovery in the presence of unobserved variables2021

Author(s)

Organizer

Data Source

[Presentation] 未観測共通原因が存在するときの因果グラフ推定2021

Author(s)

Organizer

Data Source

[Presentation] Causal additive models with unobserved variables2021

Author(s)

Organizer

Data Source

[Presentation] 統計的因果探索入門2020

Author(s)

Organizer

Data Source

Maeda TakashiNicholas 前田高志ニコラス

Takashi Nisholas Maeda 前田高志ニコラス