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This talk will provide a survey of several papers on the theory and practice of experimental design. I will compare different objectives (estimator precision, outcomes of participants, informing policy choice to maximize average outcomes, and informing policy choice to maximize utilitarian welfare), and their implications for experimental design. I will consider heuristic algorithms, will prove approximate optimality results for some of these algorithms, and will discuss several empirical applications.
Papers:
This talk is based on the following papers:
Kasy, M. (2016). Why experimenters might not always want to randomize, and what they could do instead. Political Analysis, 24(3):324–338.
maxkasy.github.io/home/files/papers/experimentaldesign.pdf
Caria, S., Gordon, G., Kasy, M., Osman, S., Quinn, S., and Teytelboym, A. (2020). Job search assistance for refugees in Jordan: An adaptive field experiment. Work in progress.
Pre-registered at www.socialscienceregistry.org/trials/3870
Kasy, M. and Sautmann, A. (2020). Adaptive treatment assignment in experiments for policy choice. Working Paper. (R&R at Econometrica)
maxkasy.github.io/home/files/papers/adaptiveexperimentspolicy.pdf
Adaptive experiments for optimal taxation, building on
Kasy, M. (2019). Optimal taxation and insurance using machine learning – sufficient statistics and beyond. Journal of Public Economics.
maxkasy.github.io/home/files/papers/PolicyDecisions.pdf
