Is Structurally Missing Data a subset of Missing at Random Data? I'm quite familiar with MCAR, MAR and MNAR (NMAR) data but I have just come across a new (for me) term: Structurally Missing Data (SMD). 
According to this page, Structurally missing data is data that is missing for a logical reason. They give an example where they ask people if they have kids and then they ask what the age of the youngest kid is. Some people answer "no" to the first question and then leave the second one blank (as there is no kids, so no age). 
But here there is a relationship between having kids and the age of youngest. Since there is a relationship, this makes the data MAR. 
So, my question is, is SMD a subset of MAR, or is there a reason why it is not? 
 A: No, I would consider Structurally Missing Data to be a separate category, with distinct methods of dealing with it in analyses. 
It is definitely not Missing at Random. By definition, it is non-random, being instead logically associated with specific values of a different variable. Let's use a lightly modified version of the example at the link: consider the variables Has_children? (yes/no) and age_of_youngest_child. If a person has no children, then age_of_youngest_child is undefined, not omitted. The missing values for age_of_youngest_child are associated logically with a specific value in Has_children?. 
Note that MAR and MCAR are frequently solved by multiple imputation, while Structurally Missing Data cannot be. 
EDIT (h/t to kjetil b halvorsen for the suggestion in the comments): 
As to how to analyse data such as this, the key is to put the nested variable into the model as an interaction term only, with no main effect. This is explain in much more detail at How do you deal with "nested" variables in a regression model? 
