38 citations · 109 across the 14 of their papers we have counts for
5 papers · 1 filter
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…