Practice causal inference: Conventional supervised learning can't do inference

This is a bit philosophical but goes into causal inference.

A trained model may provide predictions about input values it may never seen before but it isn't an inference, at least for 'classical' supervised learning. In reality it provides an interpolation from the training-set, i.e., via function approximation. By "inference implies going beyond training data", reference to distributional shift, compositional learning or similar type of learning should have been raised. 

In the case of ontology inference, ontology being a causal graph, that is a "real" inference as it symbolically traverse a graph of causal connections. Not sure if we can directly transfer that to regression scenario but probably it is possible with altering our models with SCMs and hybrid symbolic-regression approach. 

Postscript

• Looper repo provides a resource list for causal inference looper 
• Thanks to Patrick McCrae for invoking ontology inference comparison.