Tatsuhiro Shimizu

Tatsuhiro Shimizu

Hi, I am interested in Causal Inference, Machine Learning, and Natural Language Processing

Publications

Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits

Published in Proceedings of the 18th ACM Conference on Recommender Systemse, 2024

We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB).

Recommended citation: Shimizu, Tatsuhiro, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, and Yuta Saito. "Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits." In Proceedings of the 18th ACM Conference on Recommender Systems, pp. 733-741. 2024. http://tatsu432.github.io/files/OPCB.pdf

Doubly Robust Estimator for Off-Policy Evaluation with Large Action Spaces

Published in 2023 IEEE Symposium Series On Computational Intelligence, 2023

We study Off-Policy Evaluation (OPE) in contextual bandit settings with large action spaces.

Recommended citation: Shimizu, Tatsuhiro and Forastiere, Laura. (2023). "Doubly Robust Estimator for Off-Policy Evaluation with Large Action Spaces." in Proceedings of 2023 IEEE Symposium Series On Computational Intelligence. http://tatsu432.github.io/files/MDR.pdf

Diffusion Model in Causal Inference with Unmeasured Confounders

Published in 2023 IEEE Symposium Series On Computational Intelligence, 2023

We study how to extend the use of the diffusion model to answer the causal question from the observational data under the existence of unmeasured confounders.

Recommended citation: Shimizu, Tatsuhiro. (2023). "Diffusion Model in Causal Inference with Unmeasured Confounders." in Proceedings of 2023 IEEE Symposium Series On Computational Intelligence. http://tatsu432.github.io/files/BDCM.pdf