Targeted Learning for Variable Importance
This work addresses the need for more robust uncertainty quantification in variable importance metrics, which is crucial for interpreting machine learning models, though it is incremental as it builds on existing methods.
The paper tackles the problem of instability in variable importance measures in finite sample settings by applying the targeted learning framework, resulting in improved accuracy while maintaining asymptotic efficiency and computational complexity.
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.