Review for NeurIPS paper: Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data

Additional Feedback: I have read the other reviews and the authors' feedback. With the addition of the recommender system experiment, walking the readers through how (1) the MNAR and PPCA model apply in this setting, (2) selecting the hyper-parameters for the imputation algorithm, (3) showing how the imputations compare with prior algorithms, helps make a strong case for the proposed method. If the authors re-arrange the paper to improve clarity (as the reviews point out, and as they promise in their feedback), the paper can be substantially stronger. There are a few lingering questions from the reviews that the authors should address in the paper at a minimum -- (1) a discussion on a stage-wise approach to imputation (and why that may not be necessary for their sequence of regressions), (2) given that some of the linear coefficients can be zero, what must a practitioner do when one of the regressions estimate a coefficient close to 0 that is then used in the denominator of other estimates. Even better if the illustration is grounded in an example like movie item ratings.