提出两种新方法，量化机器学习模型中变量对结果的影响。
Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect
- 基于真实条件期望函数，用置换法估算变量重要性。
- 在高相关变量下，新方法能减少偏差并揭示因果效应关系。
- 适合关注模型解释性与因果推断的研究者使用。
解释黑箱机器学习模型因依赖数据且非参数特性而困难。本文重新引入基于真实条件期望函数的模型无关变量重要性度量（MVIM），评估预测变量对连续或离散结果的影响。借鉴Breiman和Fisher的方法，提出基于置换的估计策略。当预测变量高度相关时，该估计存在偏差，因黑箱模型难以在低概率区域外推。为此，我们分析了MVIM的偏差-方差分解，揭示其来源与模式。进而提出条件变量重要性度量（CVIM），源自Strobl的工作，以降低偏差。结果显示，MVIM与CVIM均与条件平均处理效应（CATE）呈二次关系。
原文摘要 · Abstract (English)
Interpreting black-box machine learning models is challenging due to their strong dependence on data and inherently non-parametric nature. This paper reintroduces the concept of importance through "Marginal Variable Importance Metric" (MVIM), a model-agnostic measure of predictor importance based on the true conditional expectation function. MVIM evaluates predictors' influence on continuous or discrete outcomes. A permutation-based estimation approach, inspired by \citet{breiman2001random} and \citet{fisher2019all}, is proposed to estimate MVIM. MVIM estimator is biased when predictors are highly correlated, as black-box models struggle to extrapolate in low-probability regions. To address this, we investigated the bias-variance decomposition of MVIM to understand the source and pattern of the bias under high correlation. A Conditional Variable Importance Metric (CVIM), adapted from \citet{strobl2008conditional}, is introduced to reduce this bias. Both MVIM and CVIM exhibit a quadratic relationship with the conditional average treatment effect (CATE).
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