While machine learning (ML) models are increasingly used due to their high
predictive power, their use in understanding the data-generating process (DGP)
is limited. Understanding the DGP requires insights into feature-target
associations, which many ML models cannot directly provide, due to their opaque
internal mechanisms. Feature importance (FI) methods provide useful insights
into the DGP under certain conditions. Since the results of different FI
methods have different interpretations, selecting the correct FI method for a
concrete use case is crucial and still requires expert knowledge. This paper
serves as a comprehensive guide to help understand the different
interpretations of FI methods. Through an extensive review of FI methods and
providing new proofs regarding their interpretation, we facilitate a thorough
understanding of these methods and formulate concrete recommendations for
scientific inference. We conclude by discussing options for FI uncertainty
estimation and point to directions for future research aiming at full
statistical inference from black-box ML models.

该论文是一项关于解释性方法在机器学习中的应用的综合指南，通过对特征重要性方法进行广泛的回顾和对其解释的新证明，为我们提供了对这些方法的深入理解，并为科学推理提供具体的建议。此外，该论文还讨论了特征重要性不确定性估计的选项，并指出了从黑盒机器学习模型获得全面统计推理的未来研究方向。