I’m a research scientist at CyberAgent, Inc.
My research interest is the intersection of Machine Learning and Economics(not only Causal Inference!). Especially, I am interested in the sampling/selection bias in the real-world application. Also, I am interested in how we can combine Mechanism Design, Causal Inference, and Machine Learning.
- Counterfactual Machine Learning
- Off-Policy Evaluation/Learning
- Machine Learning for Causal Inference
- ITE Prediction
- Uplift Modeling
- Computational Advertising
- Delayed Feedback
- Advertising Auction
- 2020.06.01: Our paper “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models” has been accepted to ICML’20.
- 2020.04.23: Our paper “ Dual Learning Algorithm for Delayed Conversions” has been accepted to SIGIR’20.
- 2020.1.18: I wrote an introductory level Causal Inference/Econometrics text book and now it is on sale! amazon.jp link
- 2020.1.10: Our paper “A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback” has been accepted to The web conference 2020!
- 2019.10.21: Our papers “Dual Learning Algorithm for Delayed Feedback in Display Advertising” and “Unbiased Pairwise Learning from Implicit Feedback” have been accepted to CausalML Workshop at NeurIPS’19.
- 2019.8.20: Our paper “Counterfactual Cross Validation” has been accepted to REVEAL Workshop at RecSys’19.
- 2019.8.20: Our paper “Reinforcement Learning meets Double Machine Learning” has been accepted to REVEAL Workshop at RecSys’19.
- 2018.12.4: Our paper “Efficient Counterfactual Learning from Bandit Feedback” has been accepted to AAAI 2019!