Nearest-neighbour matching achieves usual convergence rates under general transferability conditions on source-target distribution pairs, relaxing compact support and bounded density assumptions.
Econometrica: Journal of the Econometric Society , pages=
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Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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Nearest-Neighbour Matching on Unbounded Supports and Covariate Shift Transfer
Nearest-neighbour matching achieves usual convergence rates under general transferability conditions on source-target distribution pairs, relaxing compact support and bounded density assumptions.
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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.