将全局特征重要性分解为直接分量和关联分量（DEDACT）
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
摘要：
全局模型无关的特征重要性度量要么量化特征是否直接用于模型的预测（直接重要性），要么量化特征是否包含预测相关信息（关联重要性）。直接重要性提供了对模型机制的因果洞察，但它未能揭示相关但不直接使用的变量的信息泄漏。相比之下，关联重要性暴露信息泄漏，但不提供因果洞察模型的机制。我们介绍DEDACT -一个框架，分解成各自的关联和直接组件的直接和关联的重要性措施。DEDACT提供了对数据中预测相关信息的来源以及信息进入模型的直接和间接特征路径的洞察。我们证明了该方法的实用性模拟的例子。
英文摘要：
Global model-agnostic feature importance measures either quantify whether features are directly used for a model's predictions (direct importance) or whether they contain prediction-relevant information (associative importance). Direct importance provides causal insight into the model's mechanism, yet it fails to expose the leakage of information from associated but not directly used variables. In contrast, associative importance exposes information leakage but does not provide causal insight into the model's mechanism. We introduce DEDACT - a framework to decompose well-established direct and associative importance measures into their respective associative and direct components. DEDACT provides insight into both the sources of prediction-relevant information in the data and the direct and indirect feature pathways by which the information enters the model. We demonstrate the method's usefulness on simulated examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
