Predictive mean matching

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Predictive mean matching (PMM)^[1] is a widely used^[2] statistical imputation method for missing values, first proposed by Donald B. Rubin in 1986^[3] and R. J. A. Little in 1988.^[4]

It aims to reduce the bias introduced in a dataset through imputation, by drawing real values sampled from the data.^[5] This is achieved by building a small subset of observations where the outcome variable matches the outcome of the observations with missing values.^[1]

Compared to other imputation methods, it usually imputes less implausible values (e.g. negative incomes) and takes heteroscedastic data into account more appropriately.^[6]

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Predictive mean matching

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