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We consider the problem of quantitatively evaluating missing value imputationalgorithms. Given a dataset with missing values and a choice of severalimputation algorithms to fill them in, there is currently no principled way torank the algorithms using a quantitative metric. We develop a framework basedon treating imputation evaluation as a problem of comparing two distributionsand show how it can be used to compute quantitative metrics. We present anefficient procedure for applying this framework to practical datasets,demonstrate several metrics derived from the existing literature on comparingdistributions, and propose a new metric called Neighborhood-based DissimilarityScore which is fast to compute and provides similar results. Results are shownon several datasets, metrics, and imputations algorithms.

A Quantitative Evaluation Framework for Missing Value Imputation Algorithms