Is MI the only principled way to handle missing data?
MI is not the only principled method for handling missing values, nor is it necessarily the best for any given problem. In some cases, good estimates can be obtained through weighted estimation procedures. In fully parametric models, maximum-likelihood estimates can often be calculated directly from the incomplete data by specialized numerical methods, such as the EM algorithm. Those procedures may be somewhat more efficient than MI because they involve no simulation. Given sufficient time and resources, one could perhaps derive a better statistical procedure than MI for any particular problem. In real-life applications, however, where missing data are nuisance rather than a the primary focus, an easy, approximate solution with good properties can be preferable to one that is more efficient but problem-specific and complicated to implement.
