用黑箱模型分析特征对结果分布的影响，高效且准确。
Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution
- 基于预训练黑箱模型，近似计算特征重要性曲线
- 能捕捉特征变化对结果分位数分布的影响
- 适合关注分布变化的因果分析与可解释性研究
理解解释变量变化如何影响结果的无条件分布，在诸多应用中至关重要。然而，现有的黑箱预测模型难以直接用于此类问题分析。本文提出一种近似方法，利用预训练黑箱模型计算与结果无条件分布相关的特征重要性曲线。该曲线衡量解释变量外部变动下，结果分布各分位数的变化情况。通过大量数值实验和真实数据案例验证，所提方法能生成稀疏且忠实的结果，计算效率高。
原文摘要 · Abstract (English)
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.
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