That's not true. There's a variety of reasons why outliers can exist, for example data quality issues where an experiment failed or the method to measure the da

That's not true. There's a variety of reasons why outliers can exist, for example data quality issues where an experiment failed or the method to measure the data did not work. The approach presented there in my opinion has one big caveat: It tries to learn the cause of outliers from the data itself instead of incorporating domain knowledge! Having and using domain knowledge is time consuming and difficult, but nothing can replace it. One cannot automate away to critically explore and understand the data, although machine learning folks think they do.