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Scopus Author ID: 55749808300

Keywords

Causal Inference, Causal Discovery, Treatment Effect Estimation, Machine Learning and Fairness

Countries

Japan

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Employment (1)

NTT Communication Science Laboratories: Kyoto, JP

2015-04-01 to present | Researcher (Innovative Communication Laboratory)
Employment
Source: Self-asserted source
Yoichi Chikahara

Education and qualifications (3)

Kyoto University: Kyoto, JP

2019-10-01 to 2022-09-26 | Ph.D (Information) (Department of Intelligence Science and Technology)
Education
Source: Self-asserted source
Yoichi Chikahara

The University of Tokyo: Tokyo, JP

2013-04-01 to 2015-03-24 | Master (Information Science and Technology) (Department of Computer Science)
Education
Source: Self-asserted source
Yoichi Chikahara

Keio University: Kanagawa, JP

2009-04-01 to 2013-03-10 | Bachelor (Science) (Department of Biosciences and Informatics)
Education
Source: Self-asserted source
Yoichi Chikahara

Funding (1)

Causal Inference from Incomplete Data for Fair Machine Learning Predictions

2023-10 to 2026-03 | Grant
Japan Science and Technology Agency (Tokyo, JP)
Source: Self-asserted source
Yoichi Chikahara

Works (7)

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
2024-03-24 | Conference paper
DOI: 10.1609/aaai.v38i18.30025
Part of ISSN: 2374-3468
Part of ISSN: 2159-5399
Contributors: Shunsuke Horii; Yoichi Chikahara
Source: Self-asserted source
Yoichi Chikahara

Making individually fair predictions with causal pathways

Data Mining and Knowledge Discovery
2023-07 | Journal article
DOI: 10.1007/s10618-022-00885-6
Contributors: Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino; Hisashi Kashima
Source: check_circle
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Feature Selection for Discovering Distributional Treatment Effect Modifiers

Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022
2022 | Conference paper
EID:

2-s2.0-85146148939
Contributors: Chikahara, Y.; Yamada, M.; Kashima, H.
Source: Self-asserted source
Yoichi Chikahara via Scopus - Elsevier

Learning individually fair classifier with path-specific causal-effect constraint

International conference on artificial intelligence and statistics
2021 | Conference paper
Contributors: Chikahara, Yoichi; Sakaue, Shinsaku; Fujino, Akinori; Kashima, Hisashi
Source: Self-asserted source
Yoichi Chikahara

Causal Inference in Time Series via Supervised Learning

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
2018-07 | Other
DOI: 10.24963/ijcai.2018/282
Contributors: Yoichi Chikahara; Akinori Fujino
Source: Self-asserted source
Yoichi Chikahara via Crossref Metadata Search
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Integrative clustering of cancer genome data using infinite relational models

Proceedings of the 7th International Conference on Bioinformatics and Computational Biology, BICOB 2015
2015 | Conference paper
EID:

2-s2.0-84925856680
Contributors: Chikahara, Y.; Niida, A.; Yamaguchi, R.; Imoto, S.; Miyano, S.
Source: Self-asserted source
Yoichi Chikahara via Scopus - Elsevier

LibSBMLSim: a reference implementation of fully functional SBML simulator

Bioinformatics
2013-04-05 | Journal article
DOI: 10.1093/bioinformatics/btt157
Part of ISSN: 1460-2059
Contributors: Hiromu Takizawa; Kazushige Nakamura; Akito Tabira; Yoichi Chikahara; Tatsuhiro Matsui; Noriko Hiroi; Akira Funahashi
Source: Self-asserted source
Yoichi Chikahara via Crossref Metadata Search
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