| 2026 | AAAI | Sparse Additive Model Pruning for Order-Based Causal Structure Learning. | Kentaro Kanamori, Hirofumi Suzuki, Takuya Takagi |
| 2026 | AAAI | I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables. | Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi, Thong Pham, Takashi Nicholas Maeda, Shohei Shimizu |
| 2025 | ICML | Algorithmic Recourse for Long-Term Improvement. | Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi |
| 2024 | ICML | Learning Decision Trees and Forests with Algorithmic Recourse. | Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike |
| 2024 | PRICAI | Distribution-Aligned Sequential Counterfactual Explanation with Local Outlier Factor. | Shoki Yamao, Ken Kobayashi, Kentaro Kanamori, Takuya Takagi, Yuichi Ike, Kazuhide Nakata |
| 2022 | AISTATS | Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees. | Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike |
| 2021 | AAAI | Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization. | Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike, Kento Uemura, Hiroki Arimura |
| 2020 | IJCAI | DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization. | Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Hiroki Arimura |