Inverse Probability Weighting
A weighting method originating from importance sampling and causal inference. It weights the contribution of each sample by the inverse of its sampling probability so as to recover, in expectation, the result of full computation, thereby ensuring that the estimator is conditionally unbiased.
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Judge disagreement predicts errors, with AUROC up to 0.80, but the variance inflation from inverse probability weighting exceeds the signal gain.
Stratified sampling and inverse probability weighting are common statistical methods when evaluating rare events, restoring the true production distribution by assigning weights to samples, but they cannot make up for the insufficiency of the sample size itself.
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Judge disagreement predicts errors, with AUROC up to 0.80, but the variance inflation from inverse probability weighting exceeds the signal gain.
50%UnverifiedStratified sampling and inverse probability weighting are common statistical methods when evaluating rare events, restoring the true production distribution by assigning weights to samples, but they cannot make up for the insufficiency of the sample size itself.
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