Despite their ubiquitous use, Shapley value feature attributions can be
misleading due to feature interaction in both model and data. We propose an
alternative attribution approach, Shapley Sets, which awards value to sets of
features. Shapley Sets decomposes the underlying model into non-separable
variable groups using a recursive function decomposition algorithm with log
linear complexity in the number of variables. Shapley Sets attributes to each
non-separable variable group their combined value for a particular prediction.
We show that Shapley Sets is equivalent to the Shapley value over the
transformed feature set and thus benefits from the same axioms of fairness.
Shapley Sets is value function agnostic and we show theoretically and
experimentally how Shapley Sets avoids pitfalls associated with Shapley value
based alternatives and are particularly advantageous for data types with
complex dependency structure.

本文提出了一种替代 Shapley Value 功能归属的方法，名为 Shapley Sets，通过递归函数分解算法将基础模型分解为非可分离变量组，非常适用于具有复杂依赖结构的数据类型，并与 Shapley Value 具有相同的公平公理。