  Attribution methods have been developed to understand the decision-making
process of machine learning models, especially deep neural networks, by
assigning importance scores to individual features. Existing attribution
methods often built upon empirical intuitions and heuristics. There still lacks
a general and theoretical framework that not only can unify these attribution
methods, but also theoretically reveal their rationales, fidelity, and
limitations. To bridge the gap, in this paper, we propose a Taylor attribution
framework and reformulate seven mainstream attribution methods into the
framework. Based on reformulations, we analyze the attribution methods in terms
of rationale, fidelity, and limitation. Moreover, We establish three principles
for a good attribution in the Taylor attribution framework, i.e., low
approximation error, correct contribution assignment, and unbiased baseline
selection. Finally, we empirically validate the Taylor reformulations and
reveal a positive correlation between the attribution performance and the
number of principles followed by the attribution method via benchmarking on
real-world datasets.
