Review for NeurIPS paper: A Unified View of Label Shift Estimation

Summary and Contributions: This paper studies the label estimation problem and unifies a previously proposed perspective--maximum likelihood and calibration-- with the recent method using black-box predictors. The main takeaway is that these two perspectives can be regarded as the same framework and the calibration is a necessary step to achieve better performance. In the analysis, the weight estimation error is analyzed by decomposing it into an estimation error due to finite samples and an calibration error due to label shift. The empirical evaluation demonstrates that the combined method (MLLS with confusion matrix) outperforms using only black-box predictors. I keep my score after reading it.