  In recent years, neural networks have demonstrated their remarkable ability
to discern intricate patterns and relationships from raw data. However,
understanding the inner workings of these black box models remains challenging,
yet crucial for high-stake decisions. Among the prominent approaches for
explaining these black boxes are feature attribution methods, which assign
relevance or contribution scores to each input variable for a model prediction.
Despite the plethora of proposed techniques, ranging from gradient-based to
backpropagation-based methods, a significant debate persists about which method
to use. Various evaluation metrics have been proposed to assess the
trustworthiness or robustness of their results. However, current research
highlights disagreement among state-of-the-art methods in their explanations.
Our work addresses this confusion by investigating the explanations'
fundamental and distributional behavior. Additionally, through a comprehensive
simulation study, we illustrate the impact of common scaling and encoding
techniques on the explanation quality, assess their efficacy across different
effect sizes, and demonstrate the origin of inconsistency in rank-based
evaluation metrics.
