  This paper addresses the problem of making statistical inference about a
population that can only be identified through classifier predictions. The
problem is motivated by scientific studies in which human labels of a
population are replaced by a classifier. For downstream analysis of the
population based on classifier predictions to be sound, the predictions must
generalize equally across experimental conditions. In this paper, we formalize
the task of statistical inference using classifier predictions, and propose
bootstrap procedures to allow inference with a generalizable classifier. We
demonstrate the performance of our methods through extensive simulations and a
case study with live cell imaging data.
