Variable importance is one of the most widely used measures for interpreting machine learning with significant interest from both statistics and machine learning communities. Recently, increasing attention has been directed toward uncertainty quantification in these metrics. Current approaches largely rely on one-step procedures, which, while asymptotically efficient, can present higher sensitivity and instability in finite sample settings. To address these limitations, we propose a novel method by employing the targeted learning (TL) framework, designed to enhance robustness in inference for variable importance metrics. Our approach is particularly suited for conditional permutation variable importance. We show that it (i) retains the asymptotic efficiency of traditional methods, (ii) maintains comparable computational complexity, and (iii) delivers improved accuracy, especially in finite sample contexts. We further support these findings with numerical experiments that illustrate the practical advantages of our method and validate the theoretical results.

翻译：变量重要性是解释机器学习最广泛使用的度量之一，在统计学和机器学习领域均受到极大关注。近来，对这些指标的不确定性量化日益受到重视。现有方法主要依赖一步式程序，这些方法虽然具有渐近有效性，但在有限样本情境下可能表现出更高的敏感性和不稳定性。为应对这些局限，我们提出一种新方法，通过采用目标学习框架来增强变量重要性度量的推断稳健性。我们的方法尤其适用于条件置换变量重要性。我们证明该方法（i）保持了传统方法的渐近有效性，（ii）具有相当的计算复杂度，且（iii）尤其在有限样本情境下提供了更高的准确性。我们通过数值实验进一步支持这些发现，这些实验展示了本方法的实际优势并验证了理论结果。