MNAR -> caused by the missingness of the feature itself or an unobserved feature.
MCAR -> missingness is entirely random and has no relation to observed or unobserved features.
As a I mentioned, MNAR is complicated and requires deep inspection, domain knowledge, an understanding of the data collection process, the task we are solving, and more.
In fact, this is applicable to all datasets with missing values. You can only firmly answer with it is MNAR, MCAR or MAR once you have thoroughly inspected the dataset.
If MNAR is caused by un observed feature , how to distinguish this case from MCAR.?
Good question.
MNAR -> caused by the missingness of the feature itself or an unobserved feature.
MCAR -> missingness is entirely random and has no relation to observed or unobserved features.
As a I mentioned, MNAR is complicated and requires deep inspection, domain knowledge, an understanding of the data collection process, the task we are solving, and more.
In fact, this is applicable to all datasets with missing values. You can only firmly answer with it is MNAR, MCAR or MAR once you have thoroughly inspected the dataset.