Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated features well. However, it has two limitations. First, it does not quantify the deviation of instance-level (local) effects from the average (global) effect, known as heterogeneity. Second, for estimating the average effect, it partitions the feature domain into user-defined, fixed-sized bins, where different bin sizes may lead to inconsistent ALE estimations. To address these limitations, we propose Robust and Heterogeneity-aware ALE (RHALE). RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting. In this paper, we prove that to achieve an unbiased approximation of the standard deviation of local effects within each bin, bin splitting must follow a set of sufficient conditions. Based on these conditions, we propose an algorithm that automatically determines the optimal partitioning, balancing the estimation bias and variance. Through evaluations on synthetic and real datasets, we demonstrate the superiority of RHALE compared to other methods, including the advantages of automatic bin splitting, especially in cases with correlated features.

提出了一种名为“健壮且考虑异质性积累局部影响力(RHALE)”的解释性方法，旨在解决累积局部影响力（ALE）方法的两个局限性：无法量化局部效果与平均效果之间的异质性以及对特征域进行固定大小的分区时可能导致不一致估计的问题。通过使用标准差考虑局部效果的异质性和自动确定最佳分区，RHALE方法在合成和真实数据集上的评估表明其优于其他方法，尤其是在存在相关特征的情况下，自动分区的优势得以体现。

RHALE：稳健且异质感知的累积局部效应