  Feature attributions based on the Shapley value are popular for explaining
machine learning models; however, their estimation is complex from both a
theoretical and computational standpoint. We disentangle this complexity into
two factors: (1)~the approach to removing feature information, and (2)~the
tractable estimation strategy. These two factors provide a natural lens through
which we can better understand and compare 24 distinct algorithms. Based on the
various feature removal approaches, we describe the multiple types of Shapley
value feature attributions and methods to calculate each one. Then, based on
the tractable estimation strategies, we characterize two distinct families of
approaches: model-agnostic and model-specific approximations. For the
model-agnostic approximations, we benchmark a wide class of estimation
approaches and tie them to alternative yet equivalent characterizations of the
Shapley value. For the model-specific approximations, we clarify the
assumptions crucial to each method's tractability for linear, tree, and deep
models. Finally, we identify gaps in the literature and promising future
research directions.
