WITHDRAWN Inference on Winners after Adaptive Sampling

Oregon Convention Center 

Adaptive sampling algorithms are increasingly used in experimentation to quickly identify the best performing treatment or "winner". After running such an experiment, one is often interested in the difference in treatment effect (or difference in expected value) between the apparent "winning" treatment, as compared to a control or baseline treatment. It is well known that if one naively uses the same data to select the winning treatment and evaluate the value of the winning treatment, standard statistical approaches are invalid. Post-selection inference approaches have been developed for this inference on winners problem. However, these approaches primarily consider the i.i.d. data setting, and do not allow the data itself to be adaptively collected. In this talk, I introduce an inference approach for "winners" on adaptively collected data that utilizes randomization based methods. Our approach can be used to construct valid confidence intervals for the treatment effect between the "winner" and a baseline treatment.