Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference

ArXi:2604.18319v1 Announce Type: cross Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in epidemiological or survey settings, individuals with certain outcomes may be likely to be included, resulting in biased prevalence estimates with potentially substantial downstream impact.