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<p>Traditional variable importance measures quantify overall feature contributions but often overlook individual-level heterogeneity. Several new procedures attempt to address this limitation but remain model dependent and may introduce biases. We propose individual variable priority (iVarPro), an extension of the Variable Priority (VarPro) framework, which uses rule-based, data-driven partitioning to estimate the gradient of the conditional mean function. By focusing on gradients, iVarPro captures how small perturbations in a variable influence an individual’s outcome, providing a more precise and interpretable measure of importance. To demonstrate its advantages, we conducted simulations and analyzed a real-world survival dataset. Our results show that iVarPro more accurately captures the true functional relationship by flexibly leveraging local samples.</p>

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Individual variable priority: a model-independent local gradient method for variable importance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Min Lu,
  • Hemant Ishwaran

摘要

Traditional variable importance measures quantify overall feature contributions but often overlook individual-level heterogeneity. Several new procedures attempt to address this limitation but remain model dependent and may introduce biases. We propose individual variable priority (iVarPro), an extension of the Variable Priority (VarPro) framework, which uses rule-based, data-driven partitioning to estimate the gradient of the conditional mean function. By focusing on gradients, iVarPro captures how small perturbations in a variable influence an individual’s outcome, providing a more precise and interpretable measure of importance. To demonstrate its advantages, we conducted simulations and analyzed a real-world survival dataset. Our results show that iVarPro more accurately captures the true functional relationship by flexibly leveraging local samples.