Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.
citing papers explorer
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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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Towards a holistic understanding of Selection Bias for Causal Effect Identification
Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.