But I would argue that's not really a case of an outlier, that's a mixture distribution because the underlying identifying feature (baseball or rocket) is missing.
"There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models"
From the point of view of the data it is definitely an outlier.
> that's a mixture distribution because the underlying identifying feature (baseball or rocket) is missing.
This is the point of the paper. They literally say in the blog post summary:
To attribute the outlier event to a variable, we ask the counterfactual question “Would the event not have been an outlier had the causal mechanism of that variable been normal?”
Statisticians are known to use this kind of model. See https://en.wikipedia.org/wiki/Dirichlet_process