Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
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 more likely to be included, resulting in biased prevalence estimates with potentially substantial downstream impact. Classical corrections, such as inverse-probability weighting or explicit likelihood-based models of the selection process, rely on tractable likelihoods, which limits their applicability in complex stochastic models with latent dynamics or high-dimensional structure....
| Authors | Jonas Arruda, Sophie Chervet, Paula Staudt et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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