In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically,building onamore explicit definition ofnested counterfactuals, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.

Asmodern machinelearning systems become morepowerfulandembedded inourlives,theneed to have these systems explain their behavior becomes increasingly urgent.