LibSBMLSim: a reference implementation of fully functional SBML simulator. - Hiromu Takizawa; Kazushige Nakamura; Akito Tabira; Yoichi Chikahara; Tatsuhiro Matsui; Noriko Hiroi; Akira Funahashi (Bioinform., 2013)

Causal Inference in Time Series via Supervised Learning. - Yoichi Chikahara; Akinori Fujino (IJCAI, 2018)

Learning Individually Fair Classifier with Causal-Effect Constraint. - Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino (CoRR, 2020)

Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. - Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino; Hisashi Kashima (AISTATS, 2021)

Feature selection for discovering distributional treatment effect modifiers. - Yoichi Chikahara; Makoto Yamada; Hisashi Kashima (UAI, 2022)

Making individually fair predictions with causal pathways. - Yoichi Chikahara; Shinsaku Sakaue; Akinori Fujino; Hisashi Kashima (Data Min. Knowl. Discov., 2023)

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model. - Shunsuke Horii; Yoichi Chikahara (AAAI, 2024)

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation. - Yoichi Chikahara; Kansei Ushiyama (UAI, 2024)

Meta-learning for heterogeneous treatment effect estimation with closed-form solvers. - Tomoharu Iwata; Yoichi Chikahara (Mach. Learn., 2024)