Efficient Counterfactual Learning from Bandit Feedback. - Yusuke Narita; Shota Yasui; Kohei Yata (AAAI, 2019)

A Contextual Bandit Algorithm for Ad Creative under Ad Fatigue. - Daisuke Moriwaki; Komei Fujita; Shota Yasui; Takahiro Hoshino (CoRR, 2019)

Counterfactual Cross-Validation: Effective Causal Model Selection from Observational Data. - Yuta Saito; Shota Yasui (CoRR, 2019)

Dual Learning Algorithm for Delayed Feedback in Display Advertising. - Yuta Saito; Gota Morishita; Shota Yasui (CoRR, 2019)

Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models. - Yuta Saito; Shota Yasui (ICML, 2020)

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. - Masatoshi Uehara; Masahiro Kato; Shota Yasui (NeurIPS, 2020)

Dual Learning Algorithm for Delayed Conversions. - Yuta Saito; Gota Morishita; Shota Yasui (SIGIR, 2020)

A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback. - Shota Yasui; Gota Morishita; Komei Fujita; Masashi Shibata (WWW, 2020)

Safe Counterfactual Reinforcement Learning. - Yusuke Narita; Shota Yasui; Kohei Yata (CoRR, 2020)

A Practical Guide of Off-Policy Evaluation for Bandit Problems. - Masahiro Kato; Kenshi Abe; Kaito Ariu; Shota Yasui (CoRR, 2020)

The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy. - Masahiro Kato; Kenichiro McAlinn; Shota Yasui (NeurIPS, 2021)

Debiased Off-Policy Evaluation for Recommendation Systems. - Yusuke Narita; Shota Yasui; Kohei Yata (RecSys, 2021)

Learning Causal Relationships from Conditional Moment Conditions by Importance Weighting. - Masahiro Kato; Haruo Kakehi; Kenichiro McAlinn; Shota Yasui (CoRR, 2021)

Learning Causal Models from Conditional Moment Restrictions by Importance Weighting. - Masahiro Kato; Masaaki Imaizumi; Kenichiro McAlinn; Shota Yasui; Haruo Kakehi (ICLR, 2022)

Learning Classifiers under Delayed Feedback with a Time Window Assumption. - Shota Yasui; Masahiro Kato (KDD, 2022)

Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data. - Masahiro Kato; Shuting Wu; Kodai Kureishi; Shota Yasui (CoRR, 2023)

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. - Undral Byambadalai; Tatsushi Oka; Shota Yasui (ICML, 2024)

Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. - Tomu Hirata; Undral Byambadalai; Tatsushi Oka; Shota Yasui; Shingo Uto (CoRR, 2025)