Survey practitioners usually tackle this problem by weighting the data (so-called response weights are created by predicting the response probability based in the scare information available on non-respondents as well as adhoc assumptions of this missingness-mechanism). This task is obviously very challenging because there usually is only little information regarding the individuals not participating in the survey. So I was wondering if and how m-graphs could be used to represent this second kind of missing data? In particular, how can assumptions on whether “unit non response” is MCAR, MAR, or MNAR be encoded in a m-graph? And would any of the results on recoverability and testability stated in the paper translate to this second problem?

]]>