Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
International Journal of Epidemiology , volume=
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Tutorial reviewing and comparing methods to correct measurement error in outcomes and multiple covariates, with a running example, data, and code for reproduction.
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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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Methods to address measurement error in both Outcome and Covariates
Tutorial reviewing and comparing methods to correct measurement error in outcomes and multiple covariates, with a running example, data, and code for reproduction.