38 citations · 109 across the 14 of their papers we have counts for
14 papers
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita +3
Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluatio…
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…
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Takuma Udagawa, Haruka Kiyohara, Yusuke Narita +2
Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, the…
Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials
Xinru Wang, Nina Deliu, Yusuke Narita +1
Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients' time-varying clinical conditions. The sequential multiple assignment r…
Approximating Choice Data by Discrete Choice Models
Haoge Chang, Yusuke Narita, Kota Saito
We obtain a necessary and sufficient condition under which random-coefficient discrete choice models, such as mixed-logit models, are rich enough to approximate any nonparametric r…
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…
Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
Yusuke Narita, Kohei Yata
Algorithms make a growing portion of policy and business decisions. We develop a treatment-effect estimator using algorithmic decisions as instruments for a class of stochastic and…
Democracy and Growth in the 21st Century
Yusuke Narita
We find that, in the 21st century, democracy has persistent negative impacts on growth in GDP and night-time light intensity. This finding emerges from five different instrumental…
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…
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Yuta Saito, Shunsuke Aihara, Megumi Matsutani +1
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. Because of its huge potential impact in practice,…
Debiased Off-Policy Evaluation for Recommendation Systems
Yusuke Narita, Shota Yasui, Kohei Yata
Efficient methods to evaluate new algorithms are critical for improving interactive bandit and reinforcement learning systems such as recommendation systems. A/B tests are reliable…
Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
Masahiro Kato, Takuya Ishihara, Junya Honda +1
We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimen…
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…