In reply to AnnMaria De Mars.

Thanks for your thoughts and anecdote. I agree that an analyst should look into causes of missingness before blindly proceeding with the analysis.
Since you mentioned the missing at random (MAR) assumption, I want to add a few thoughts:

1. The assumption is often used to assess the bias of estimators. For the regression example, I believe that if the X are MAR, then the expected value of the intercept for the imputed variable is same as the intercept for the missing data. Unfortunately, even if a theorem shows that an estimate is unbiased "on average," for a particular set of data (such as the regression example) the missing X values might correspond to Y values that are larger (or smaller) than expected.
2. For small data sets, it can be hard to verify whether values are MAR.
3. Although MAR assumption can help you assess bias in point estimates, it doesn't change the most damning aspect of mean imputation, which is the shrunken variance estimates.

Thanks for mentioning MAR. There is much more that can (and has!) been said on this topic.