Can Machine Learning Models Recover "Experimental, Design and Hierarchical Structures" Within the Data?
At times, real world data can contain "embedded structures" - these structures are sometimes "controlled" (e.g. variation in chemistry exam scores from different countries) and sometimes they are "uncontrolled" (e.g. variation in chemistry exam scores within the same class). In the context of statistical modelling, these "structures" can often assist and guide us - by encouraging to analyze data and build separate models for data aggregated at different structural and sub-structural levels. We may even be interested in estimating the average "effects" that these structures have on our data - treating these effects themselves as random variables. For more information , please consult the following topics:
- Random, Fixed and Mixed Effects
- Hierarchical Models
- Nested Models
- Multilevel Models
However, I have the following question: Without being "told" about the existence of these structures in the data, can Machine Learning Models (e.g. Random Forest, Gradient Boosting) still perform well by latently modelling the effects of these structures?
If they can do so - this could be beneficial in these sense that Machine Learning Models might be able to "detect" convoluted structures embedded in the data (e.g. 3rd Level: by ZIP Code, School and Faculty) that might exist and have substantial effects - but are currently unknown to the researchers. However, I can imagine that even if the Machine Learning Models are able to make reliable predictions in the presence of detailed hierarchical structures in the data, they will not be able to identify these structures as easily - ultimately preventing a deeper understanding of such structures. I suppose that standard clustering methods might be applicable in recovering these structures and then training Machine Learning Models on each of these clusters - but the clusters run the risk of being sparse and uninformative. This leads me into believing that it still might be ideal to prefer classical statistical models where these kinds of effects and structures are directly modelled, instead of being passively analyzed in the background.
Can someone please provide some comments on this?