IDEAS home Printed from https://ideas.repec.org/r/ces/ceswps/_7778.html
   My bibliography  Save this item

Adaptive Treatment Assignment in Experiments for Policy Choice

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Julia Höhler & Jesús Barreiro‐Hurlé & Mikołaj Czajkowski & François J. Dessart & Paul J. Ferraro & Tongzhe Li & Kent D. Messer & Leah Palm‐Forster & Mette Termansen & Fabian Thomas & Katarzyna Zagórsk, 2024. "Perspectives on stakeholder participation in the design of economic experiments for agricultural policymaking: Pros, cons, and twelve recommendations for researchers," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(1), pages 338-359, March.
  2. Karun Adusumilli, 2021. "Risk and optimal policies in bandit experiments," Papers 2112.06363, arXiv.org, revised Jan 2024.
  3. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
  4. Battistin,Erich & De Nadai,Michele & Krishnan,Nandini, 2020. "The Insights and Illusions of Consumption Measurements," Policy Research Working Paper Series 9255, The World Bank.
  5. Glenn W. Harrison, 2024. "Risk preferences and risk perceptions in insurance experiments: some methodological challenges," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 127-161, March.
  6. Nicolo Cesa-Bianchi & Roberto Colomboni & Maximilian Kasy, 2023. "Adaptive maximization of social welfare," Papers 2310.09597, arXiv.org, revised Jul 2024.
  7. A Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Osman Shami & Alexander Teytelboym, 2024. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," Journal of the European Economic Association, European Economic Association, vol. 22(2), pages 781-836.
  8. Elena Serfilippi & Daniele Giovannucci & David Ameyaw & Ankur Bansal & Thomas Asafua Nketsia Wobill & Roberta Blankson & Rashi Mishra, 2022. "Benefits and Challenges of Making Data More Agile: A Review of Recent Key Approaches in Agriculture," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
  9. Frederico Finan & Demian Pouzo, 2021. "Reinforcing RCTs with Multiple Priors while Learning about External Validity," Papers 2112.09170, arXiv.org, revised Sep 2024.
  10. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
  11. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org.
  12. Harrison H. Li & Art B. Owen, 2023. "Double machine learning and design in batch adaptive experiments," Papers 2309.15297, arXiv.org.
  13. Battistin, Erich & De Nadai, Michele & Krishnan, Nandini, 2023. "The insights and illusions of consumption measurements," Journal of Development Economics, Elsevier, vol. 161(C).
  14. Karun Adusumilli, 2023. "Optimal tests following sequential experiments," Papers 2305.00403, arXiv.org, revised Jun 2023.
  15. Xiaoxue Sherry Gao & Glenn W. Harrison & Rusty Tchernis, 2020. "Behavioral Welfare Economics and Risk Preferences: A Bayesian Approach," NBER Working Papers 27685, National Bureau of Economic Research, Inc.
  16. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
  17. Bahety, Girija & Bauhoff, Sebastian & Patel, Dev & Potter, James, 2021. "Texts don’t nudge: An adaptive trial to prevent the spread of COVID-19 in India," Journal of Development Economics, Elsevier, vol. 153(C).
  18. Chao Qin & Daniel Russo, 2024. "Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification," Papers 2402.10592, arXiv.org, revised Jul 2024.
  19. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
  20. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
  21. Michael Walton, 2023. "Adaptive Evaluation: A Complexity-Based Approach to Systematic Learning for Innovation and Scaling in Development," CID Working Papers 428, Center for International Development at Harvard University.
  22. Nibbering, Didier & Oosterveen, Matthijs & Silva, Pedro Luís, 2022. "Clustered Local Average Treatment Effects: Fields of Study and Academic Student Progress," IZA Discussion Papers 15159, Institute of Labor Economics (IZA).
  23. Qian Qi, 2023. "Artificial Intelligence and Dual Contract," Papers 2303.12350, arXiv.org, revised Jun 2024.
  24. Maximilian Kasy & Alexander Teytelboym, 2020. "Adaptive targeted infectious disease testing," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 36(Supplemen), pages 77-93.
  25. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2023. "Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds," Papers 2302.02988, arXiv.org, revised Jul 2023.
  26. Masahiro Kato & Kyohei Okumura & Takuya Ishihara & Toru Kitagawa, 2024. "Adaptive Experimental Design for Policy Learning," Papers 2401.03756, arXiv.org, revised Feb 2024.
  27. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
  28. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  29. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
  30. Samantha Horn & Sabina J. Sloman, 2022. "A Comparison of Methods for Adaptive Experimentation," Papers 2207.00683, arXiv.org.
  31. Caio Waisman & Harikesh S. Nair & Carlos Carrion, 2019. "Online Causal Inference for Advertising in Real-Time Bidding Auctions," Papers 1908.08600, arXiv.org, revised Feb 2024.
  32. Masahiro Kato, 2021. "Adaptive Doubly Robust Estimator from Non-stationary Logging Policy under a Convergence of Average Probability," Papers 2102.08975, arXiv.org, revised Mar 2021.
  33. Toru Kitagawa & Guanyi Wang, 2021. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," CeMMAP working papers CWP28/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  34. Maximilian Kasy & Alexander Teytelboym, 2023. "Matching with semi-bandits," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 45-66.
  35. Xiaoxue Sherry Gao & Glenn W. Harrison & Rusty Tchernis, 2023. "Behavioral welfare economics and risk preferences: a Bayesian approach," Experimental Economics, Springer;Economic Science Association, vol. 26(2), pages 273-303, April.
  36. Sebastian Jobjörnsson & Henning Schaak & Oliver Musshoff & Tim Friede, 2023. "Improving the statistical power of economic experiments using adaptive designs," Experimental Economics, Springer;Economic Science Association, vol. 26(2), pages 357-382, April.
  37. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
  38. Maximilian Kasy & Alexander Teytelboym, 2020. "Adaptive Combinatorial Allocation," Papers 2011.02330, arXiv.org.
  39. Toru Kitagawa & Jeff Rowley, 2024. "Bandit Algorithms for Policy Learning: Methods, Implementation, and Welfare-performance," Papers 2409.00379, arXiv.org.
  40. Gaul, Johannes J. & Keusch, Florian & Rostam-Afschar, Davud & Simon, Thomas, 2024. "Invitation Messages for Business Surveys: A Multi-Armed Bandit Experiment," IZA Discussion Papers 17534, Institute of Labor Economics (IZA).
  41. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
  42. Danielle Li & Lindsey R. Raymond & Peter Bergman, 2020. "Hiring as Exploration," NBER Working Papers 27736, National Bureau of Economic Research, Inc.
  43. Masahiro Kato, 2024. "Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification," Papers 2405.19317, arXiv.org, revised Dec 2024.
  44. Larissa Fuchs & Matthias Heinz & Pia Pinger & Max Thon, 2024. "How to Attract Talents? Field-Experimental Evidence on Emphasizing Flexibility and Career Opportunities in Job Advertisements," ECONtribute Discussion Papers Series 332, University of Bonn and University of Cologne, Germany.
  45. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2022. "Best Arm Identification with Contextual Information under a Small Gap," Papers 2209.07330, arXiv.org, revised Jan 2023.
  46. Karun Adusumilli, 2022. "Neyman allocation is minimax optimal for best arm identification with two arms," Papers 2204.05527, arXiv.org, revised Aug 2022.
  47. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
  48. Susan Athey & Undral Byambadalai & Vitor Hadad & Sanath Kumar Krishnamurthy & Weiwen Leung & Joseph Jay Williams, 2022. "Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning," Papers 2211.12004, arXiv.org.
  49. Toru Kitagawa & Jeff Rowley, 2024. "Bandit algorithms for policy learning: methods, implementation, and welfare-performance," The Japanese Economic Review, Springer, vol. 75(3), pages 407-447, July.
  50. Masahiro Kato, 2023. "Locally Optimal Fixed-Budget Best Arm Identification in Two-Armed Gaussian Bandits with Unknown Variances," Papers 2312.12741, arXiv.org, revised Mar 2024.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.