  Shapley values have become one of the go-to methods to explain complex models
to end-users. They provide a model agnostic post-hoc explanation with
foundations in game theory: what is the worth of a player (in machine learning,
a feature value) in the objective function (the output of the complex machine
learning model). One downside is that they always require outputs of the model
when some features are missing. These are usually computed by taking the
expectation over the missing features. This however introduces a non-trivial
choice: do we condition on the unknown features or not? In this paper we
examine this question and claim that they represent two different explanations
which are valid for different end-users: one that explains the model and one
that explains the model combined with the feature dependencies in the data. We
propose a new algorithmic approach to combine both explanations, removing the
burden of choice and enhancing the explanatory power of Shapley values, and
show that it achieves intuitive results on simple problems. We apply our method
to two real-world datasets and discuss the explanations. Finally, we
demonstrate how our method is either equivalent or superior to state-to-of-art
Shapley value implementations while simultaneously allowing for increased
insight into the model-data structure.
