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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.
Under-representation of certain populations, based on gender, race/ethnicity, and age, in data collection for predictive modeling may yield less-accurate predictions for the under-represented groups. Recently, this issue of fairness in predictions ha
Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this
The $DDalpha$-classifier, a nonparametric fast and very robust procedure, is described and applied to fifty classification problems regarding a broad spectrum of real-world data. The procedure first transforms the data from their original property sp
Causal mediation analysis is used to evaluate direct and indirect causal effects of a treatment on an outcome of interest through an intermediate variable or a mediator.It is difficult to identify the direct and indirect causal effects because the me
We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in re