  Universal function approximators, such as artificial neural networks, can
learn a large variety of target functions arbitrarily well given sufficient
training data. This flexibility comes at the cost of the ability to perform
parametric inference. We address this gap by proposing a generic framework
based on the Shapley-Taylor decomposition of a model. A surrogate parametric
regression analysis is performed in the space spanned by the Shapley value
expansion of a model. This allows for the testing of standard hypotheses of
interest. At the same time, the proposed approach provides novel insights into
statistical learning processes themselves derived from the consistency and bias
properties of the nonparametric estimators. We apply the framework to the
estimation of heterogeneous treatment effects in simulated and real-world
randomised experiments. We introduce an explicit treatment function based on
higher-order Shapley-Taylor indices. This can be used to identify potentially
complex treatment channels and help the generalisation of findings from
experimental settings. More generally, the presented approach allows for a
standardised use and communication of results from machine learning models.
