Publications

Kohsuke Kubota, Mitsuhiro Takahashi, Yuta Saito (2026). Off-Policy Evaluation and Learning for Survival Outcomes under Censoring. arXiv preprint arXiv:2603.22900.

Kohsuke Kubota, Shonosuke Sugasawa (2026). Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects. In Proceedings of the The 40th Annual AAAI Conference on Artificial Intelligence (AAAI) (Acceptance Rate = 17.6%).

Takumi Hattori, Kohsuke Kubota, Keiichi Ochiai (2026). Wald-Difference-in-Differences Estimation without Individual-level Treatment Data. In Journal of Information Processing.

Kohsuke Kubota, Takahiro Hoshino (2025). Impact of lottery promotion wins and losses: evidence from a promotion in a mobile payment service. In Asia Pacific Journal of Marketing and Logistics (Impact Factor = 5.1, Acceptance Rate = 11.4%).

Yuki Murakami, Takumi Hattori, Kohsuke Kubota (2025). Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning. arXiv preprint arXiv:2511.09814.

Yuki Murakami, Kohsuke Kubota, Takumi Hattori, Keiichi Ochiai (2025). Estimation of Single and Synergistic Treatment Effects under Multiple Treatments with Deep Neural Networks. In KDD 2025 3rd Workshop on Causal Inference and Machine Learning in Practice.

Kohsuke Kubota, Shonosuke Sugasawa (2025). Causal Inference under Threshold Manipulation: A Bayesian Mixture Approach. In KDD 2025 3rd Workshop on Causal Inference and Machine Learning in Practice.

Kohsuke Kubota, Shonosuke Sugasawa, Keiichi Ochiai, Takahiro Hoshino (2025). Bayesian Time-Varying Meta-Analysis via Hierarchical Mean-Variance Random-effects Models. In Japanese Journal of Statistics and Data Science (Impact Factor = 1.0).

Kohsuke Kubota, Keiichi Ochiai, Takahiro Hoshino (2025). Causal effect of lottery promotions on post-win payments: Evidence from a large field experiment . In Innovative Marketing (Impact Factor = 1.2, Acceptance Rate = 33%).

Takumi Hattori, Kohsuke Kubota, Keiichi Ochiai (2025). Wald-Differences-in-Differences Estimation without Individual-Level Treatment Data. In AAAI’25 Workshop on Artificial Intelligence with Causal Techniques.