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Special feature on recent developments in causal inference and machine learning, Vol.2 (2024) co-edited with Shuichi Kawano
Special feature on recent developments in causal inference and machine learning, Vol.2 (2024) co-edited with Shuichi Kawano
Masayoshi Takayanagi, Mutsumi Yoshino, Genta Kikuchi, Tomoko Kanke, and Noriyuki Suzuki. Autonomous adaptive control of manufacturing parameters based on local regression modeling. Behaviormetrika, 51: 499–513, 2024.
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Konstantin Genin and Conor Mayo-Wilson. Success concepts for causal discovery. Behaviormetrika, 51: 515–538, 2024.
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Jun Otsuka and Hayato Saigo. Process theory of causality: a category-theoretic perspective. Behaviormetrika, 51: 21–36, 2024.
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Special feature on recent developments in causal inference and machine learning, Vol.1 (2022) co-edited with Shuichi Kawano
Special feature on recent developments in causal inference and machine learning, Vol.1 (2022) co-edited with Shuichi Kawano
Michael U. Gutmann, Steven Kleinegesse, and Benjamin Rhodes. Statistical applications of contrastive learning. Behaviormetrika, 49: 277–301, 2022.
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Wolfgang Wiedermann. Third moment-based causal inference. Behaviormetrika, 49: 303–328, 2022.
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Takashi Nicholas Maeda. I-RCD: an improved algorithm of repetitive causal discovery from data with latent confounders. Behaviormetrika, 49: 329–341, 2022.
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Jalal Etesami, Kun Zhang, and Negar Kiyavash. A Wasserstein-based measure of conditional dependence. Behaviormetrika, 49: 343–362, 2022.
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Special feature on recent developments in causal discovery and inference (2017)
Special feature on recent developments in causal discovery and inference (2017)
Krzysztof Chalupka, Frederick Eberhardt, and Pietro Perona. Causal feature learning: an overview. Behaviormetrika, 44(1): 137–164, 2017.
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Sisi Ma and Alexander Statnikov. Methods for computational causal discovery in biomedicine. Behaviormetrika, 44(1), 165-191, 2017.
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Teague Henry and Kathleen Gates. Causal search procedures for fMRI: review and suggestions. Behaviormetrika, 44(1), 193--225, 2017.
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Special issue on causal discovery (2014) co-edited with Jun-ichiro Hirayama
Special issue on causal discovery (2014) co-edited with Jun-ichiro Hirayama
Ilya Shpitser, Robin Evans, Thomas S. Richardson, and James M. Robins. Introduction to nested Markov models. Behaviormetrika, 41(1): 3--39, 2014.
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Robert E. Tillman and Frederick Eberhardt. Learning causal structure from multiple datasets with similar variable sets. Behaviormetrika, 41(1): 41--64, 2014.
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Shohei Shimizu. LiNGAM: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 41(1): 65--98, 2014.
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