Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to compute the feature importance curves relevant to the unconditional distribution of outcomes, while leveraging the power of pre-trained black-box predictive models. The feature importance curves measure the changes across quantiles of outcome distribution given an external impact of change in the explanatory features. Through extensive numerical experiments and real data examples, we demonstrate that our approximation method produces sparse and faithful results, and is computationally efficient.

翻译：理解解释性特征的变化如何影响结果的无条件分布在许多应用中具有重要意义。然而，现有的黑盒预测模型并不直接适用于分析此类问题。在本研究中，我们开发了一种近似计算方法，用于计算与结果无条件分布相关的特征重要性曲线，同时充分利用预训练黑盒预测模型的能力。特征重要性曲线衡量了在解释性特征受到外部影响发生变化时，结果分布各分位数上的变化程度。通过大量数值实验和真实数据示例，我们证明所提出的近似方法能够生成稀疏且可靠的结果，并具有较高的计算效率。