Variable importance plays a pivotal role in interpretable machine learning as
it helps measure the impact of factors on the output of the prediction model.
Model agnostic methods based on the generation of "null" features via
permutation (or related approaches) can be applied. Such analysis is often
utilized in pharmaceutical applications due to its ability to interpret
black-box models, including tree-based ensembles. A major challenge and
significant confounder in variable importance estimation however is the
presence of between-feature correlation. Recently, several adjustments to
marginal permutation utilizing feature knockoffs were proposed to address this
issue, such as the variable importance measure known as conditional predictive
impact (CPI). Assessment and evaluation of such approaches is the focus of our
work. We first present a comprehensive simulation study investigating the
impact of feature correlation on the assessment of variable importance. We then
theoretically prove the limitation that highly correlated features pose for the
CPI through the knockoff construction. While we expect that there is always no
correlation between knockoff variables and its corresponding predictor
variables, we prove that the correlation increases linearly beyond a certain
correlation threshold between the predictor variables. Our findings emphasize
the absence of free lunch when dealing with high feature correlation, as well
as the necessity of understanding the utility and limitations behind methods in
variable importance estimation.

变量重要性在可解释机器学习中起着关键作用，它有助于衡量因素对预测模型输出的影响。我们的研究重点是评估和评估旨在解决变量重要性评估中特征相关性问题的方法，如条件预测影响（CPI）等，我们首先进行综合模拟研究，调查特征相关性对变量重要性评估的影响，然后通过 knockoff 构造从理论上证明高相关特征对 CPI 的限制。我们的发现强调了处理高特征相关性时的困难和方法在变量重要性评估中的实用性和局限性的必要性。