Yoichi Chikahara

According to our database, Yoichi Chikahara authored at least 12 papers between 2013 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables.

Yoichi Chikahara

CoRR, February, 2026

Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data.

Yoichi Chikahara

CoRR, February, 2026

2025

MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions.

Hans Jarett J. Ong, Yoichi Chikahara, Tomoharu Iwata

CoRR, October, 2025

2024

Meta-learning for heterogeneous treatment effect estimation with closed-form solvers.

Tomoharu Iwata, Yoichi Chikahara

Mach. Learn., September, 2024

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation.

Yoichi Chikahara, Kansei Ushiyama

Proceedings of the Uncertainty in Artificial Intelligence, 2024

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

Shunsuke Horii, Yoichi Chikahara

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Making individually fair predictions with causal pathways.

Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima

Data Min. Knowl. Discov., 2023

2022

Feature selection for discovering distributional treatment effect modifiers.

Yoichi Chikahara, Makoto Yamada, Hisashi Kashima

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint.

Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning Individually Fair Classifier with Causal-Effect Constraint.

Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino

CoRR, 2020

2018

Causal Inference in Time Series via Supervised Learning.

Yoichi Chikahara, Akinori Fujino

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2013

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