Research design meets market design: Using centralized assignment for impact evaluation A Abdulkadiroğlu, JD Angrist, Y Narita, PA Pathak Econometrica 85 (5), 1373-1432, 2017 | 260 | 2017 |
Research design meets market design: Using centralized assignment for impact evaluation A Abdulkadiroğlu, JD Angrist, Y Narita, PA Pathak Econometrica 85 (5), 1373-1432, 2017 | 260 | 2017 |
Research design meets market design: Using centralized assignment for impact evaluation A Abdulkadiroğlu, JD Angrist, Y Narita, PA Pathak Econometrica 85 (5), 1373-1432, 2017 | 260 | 2017 |
Breaking ties: Regression discontinuity design meets market design A Abdulkadı̇roğlu, JD Angrist, Y Narita, P Pathak Econometrica 90 (1), 117-151, 2022 | 82 | 2022 |
Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation Y Saito, S Aihara, M Matsutani, Y Narita arXiv preprint arXiv:2008.07146, 2020 | 78 | 2020 |
Match or mismatch? Learning and inertia in school choice Y Narita Learning and Inertia in School Choice (June 18, 2018), 2018 | 62 | 2018 |
Improving schools through school choice: A market design approach JW Hatfield, F Kojima, Y Narita Journal of Economic Theory 166, 186-211, 2016 | 60 | 2016 |
Guilt aversion revisited: An experimental test of a new model T Kawagoe, Y Narita Journal of Economic Behavior & Organization 102, 1-9, 2014 | 54 | 2014 |
Efficient Counterfactual Learning from Bandit Feedback Y Narita, S Yasui, K Yata arXiv preprint arXiv:1809.03084, 2018 | 47 | 2018 |
Doubly robust off-policy evaluation for ranking policies under the cascade behavior model H Kiyohara, Y Saito, T Matsuhiro, Y Narita, N Shimizu, Y Yamamoto Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | 44 | 2022 |
Regression discontinuity in serial dictatorship: Achievement effects at Chicago's exam schools A AbdulkadIroğlu, JD Angrist, Y Narita, PA Pathak, RA Zarate American Economic Review 107 (5), 240-245, 2017 | 43 | 2017 |
Promoting school competition through school choice: A market design approach JW Hatfield, F Kojima, Y Narita Stanford: Stanford University Capital and Economic Opportunity Working Group …, 2011 | 38 | 2011 |
Evaluating the robustness of off-policy evaluation Y Saito, T Udagawa, H Kiyohara, K Mogi, Y Narita, K Tateno Proceedings of the 15th ACM Conference on Recommender Systems, 114-123, 2021 | 34 | 2021 |
Many-to-many matching with max–min preferences JW Hatfield, F Kojima, Y Narita Discrete Applied Mathematics 179, 235-240, 2014 | 23 | 2014 |
Curse of Democracy: Evidence from 2020 Y Narita, A Sudo Available at SSRN 3827327, 2021 | 20 | 2021 |
Efficient adaptive experimental design for average treatment effect estimation M Kato, T Ishihara, J Honda, Y Narita arXiv preprint arXiv:2002.05308, 2020 | 18 | 2020 |
Policy-adaptive estimator selection for off-policy evaluation T Udagawa, H Kiyohara, Y Narita, Y Saito, K Tateno Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 10025 …, 2023 | 14 | 2023 |
Debiased off-policy evaluation for recommendation systems Y Narita, S Yasui, K Yata Proceedings of the 15th ACM Conference on Recommender Systems, 372-379, 2021 | 13 | 2021 |
Incorporating ethics and welfare into randomized experiments Y Narita Proceedings of the National Academy of Sciences 118 (1), e2008740118, 2021 | 13 | 2021 |
Adaptive experimental design for efficient treatment effect estimation: Randomized allocation via contextual bandit algorithm M Kato, T Ishihara, J Honda, Y Narita arXiv preprint arXiv:2002.05308 10, 32, 2020 | 13 | 2020 |