Design-Driven Inference for Online Experiments: Jointly Optimal Evidence Collection, Testing, and Estimation
在线实验的设计驱动推断:证据收集、检验与估计的联合最优性
Online multifactorial experiments requires early stopping for practically negligible effects while controlling false stopping under continuous monitoring and retaining precise treatment-effect estimation. We develop a design-driven framework that chooses allocation and the evidence rule jointly, treating allocation as evidence collection and testing and estimation as complementary uses of the same evidence. For multifactor experiments with nuisance block effects and…