Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various fields. The present paper attempts to take a step back and provide a more systematic analysis. Starting from an in-depth discussion of the Missing at Random (MAR) condition for nonparametric imputation, we first develop an identification result, showing that the widely used Multiple Imputation by Chained Equations (MICE) approach indeed identifies the right conditional distributions. Building on this analysis, we propose three essential properties a successful imputation method should meet, thus enabling a more principled evaluation of existing methods and more targeted development of new methods. In particular, we introduce a new imputation method, denoted mice-DRF, that meets two out of the three criteria. We then discuss and refine ways to rank imputation methods, developing a powerful, easy-to-use scoring algorithm to rank missing value imputations.