Special Issues and Events | 特集号とイベント

Invited speakers:
Gunwoong Park, University of Seoul, Korea
Hiroshi Takeuchi, Shiga University, Japan
Ioannis Tsamardinos, University of Crete and Gnosis Data Analysis, Greece
Kun Zhang, Carnegie Mellon University, USA
Masayoshi Takayanagi, Shiga University, Japan
Michael Gutmann, University of Edinburgh, UK
Negar Kiyavash, Ecole polytechnique fédérale de Lausanne, Switzerland
Ricardo Pio Monti, Facebook Reality Labs, UK
Sara Magliacane, University of Amsterdam, Netherlands
Takashi Nicholas Maeda, RIKEN, Japan
Wolfgang Wiedermann, University of Missouri, USA

Invited speakers:
Cesare Alippi, Politecnico di Milano, Italy
Chunchen Liu, Damo academy, Alibaba group, China
Colleen Ruan, NVIDIA, Japan
Daniel Malinsky, Columbia University, USA
Ke Yan, National University of Singapore, Singapore
Matthew J. Holland, Osaka University, Japan
MingMing Gong, The University of Melbourne, Australia
Moritz Marutschke, Ritsumeikan University, Japan
Paskorn Apirukvorapinit, Thai-Nichi Institute of Technology, Thailand
Patrick Bloebaum, Amazon Research Tubingen, Germany
Samuel Wang, University of Chicago’s Booth School of Business, USA

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.
[pdf] [Google scholar]

Sisi Ma and Alexander Statnikov. Methods for computational causal discovery in biomedicine. Behaviormetrika, 44(1), 165-191, 2017.
[pdf] [Google scholar]

Teague Henry and Kathleen Gates. Causal search procedures for fMRI: review and suggestions. Behaviormetrika, 44(1), 193--225, 2017.
[pdf] [Google scholar]

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.
[pdf] [Google scholar]

Robert E. Tillman and Frederick Eberhardt. Learning causal structure from multiple datasets with similar variable sets. Behaviormetrika, 41(1): 41--64, 2014.
[pdf] [Google scholar]

Shohei Shimizu. LiNGAM: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 41(1): 65--98, 2014.
[pdf] [Google scholar]