ABSTRACT | 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.
BIO | Maximilian is currently an Associate Professor at the Department of Economics at University of Oxford. Previously, he was an associate professor at the Department of Economics at Harvard University as well as at University of California. He holds his Ph.D. in Economics from UC Berkeley. He has publications (among others) in American Economic Review, in the Econometrics Journal, in Review of Economic Studies, in Review of Economics and Statistics, in Political Analysis and in the Journal of Public Economics. His research interests include statistical decision theory (applied to experimental design, machine learning, policy choice, and empirical Bayes methods); statistics as a social process (publication bias); the use of economic theory in econometrics; economic inequality and (optimal) taxation.