A regulator-constrained optimization framework for experimental design that makes Neyman allocation optimal under normal priors and achieves over 48% sample-size reduction relative to classical designs at the same welfare level.
C.5.Examination of alternative methods for calibratingBn.In this section, we reproduce the analysis from Section 4.4, but under alternative calibrations forBn
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Designing Persuasive Experiments
A regulator-constrained optimization framework for experimental design that makes Neyman allocation optimal under normal priors and achieves over 48% sample-size reduction relative to classical designs at the same welfare level.