38 citations · 63 across the 4 of their papers we have counts for
5 papers
Counterfactual Learning with General Data-generating Policies
Yusuke Narita, Kyohei Okumura, Akihiro Shimizu +1
Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE…
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro +3
In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking p…
Evaluating the Robustness of Off-Policy Evaluation
Yuta Saito, Takuma Udagawa, Haruka Kiyohara +3
Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in app…
Breaking Ties: Regression Discontinuity Design Meets Market Design
Atila Abdulkadiroglu, Joshua D. Angrist, Yusuke Narita +1
Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over-subscribed schools using randomly assigned lott…
Efficient Counterfactual Learning from Bandit Feedback
Yusuke Narita, Shota Yasui, Kohei Yata
What is the most statistically efficient way to do off-policy evaluation and optimization with batch data from bandit feedback? For log data generated by contextual bandit algorith…