Rules of thumb for when imputation of missing values should not be used.

imputation_rules.md

When imputation should not be used

• If data are MCAR, imputation may not be not needed.
• If missingness is due to unmeasured variables related to the dependent variable, data are MNAR and should not be imputed.
• Imputation assumes data are MAR and should not be used with sparse data. Sparse data occur when missingness is non-random, such as a shopping cart survey of items purchased (coded 1) or not purchased (coded 0), because the null response (0) is non-random, due to unmeasured factors possibly not even known to the shopper.
• Imputation should not be used to impute all the data for a subject
• Imputation should not be used for a missing value for a given observation if that observation is also missing values on predictively critical variables in the imputation model. While this is difficult to check for each value to be imputed, a table of missing value patterns will show how many cases missing on a given variable also have missing values on other variables. In some cases this may lead a researcher to reject imputation.
• Imputation should not be used if over 50% of data are missing (some authors use lower cutoffs, such as 20%).
• Imputation is used with cross-sectional or historical data and is not appropriate for imputing future data in a time series.
• Use of imputation is suspect if it generates values outside valid ranges.
• Imputation based on a single pass is not acceptable due to the probabilistic nature of imputation. While as few as 3 – 5 imputations may suffice for reliability, today 20 – 100 or more imputations are usual.

Sources

• http://www.statisticalassociates.com/missingvaluesanalysis_p.pdf