We analyze two widely used local attribution methods, Local Shapley Values and LIME, which aim to quantify the contribution of a feature value $x_i$ to a specific prediction $f(x_1, \dots, x_p)$. Despite their widespread use, we identify fundamental limitations in their ability to reliably detect locally important features, even under ideal conditions with exact computations and independent features. We argue that a sound local attribution method should not assign importance to features that neither influence the model output (e.g., features with zero coefficients in a linear model) nor exhibit statistical dependence with functionality-relevant features. We demonstrate that both Local SV and LIME violate this fundamental principle. To address this, we propose R-LOCO (Regional Leave Out COvariates), which bridges the gap between local and global explanations and provides more accurate attributions. R-LOCO segments the input space into regions with similar feature importance characteristics. It then applies global attribution methods within these regions, deriving an instance's feature contributions from its regional membership. This approach delivers more faithful local attributions while avoiding local explanation instability and preserving instance-specific detail often lost in global methods.

翻译：我们分析了两种广泛使用的局部归因方法——局部Shapley值与LIME。这两种方法旨在量化特征值$x_i$对特定预测$f(x_1, \dots, x_p)$的贡献度。尽管它们被广泛应用，我们仍发现其在可靠检测局部重要特征方面存在根本性局限——即便在精确计算且特征相互独立的理想条件下也是如此。我们认为，一个合理的局部归因方法不应将重要性赋予那些既不影响模型输出（例如线性模型中系数为零的特征）又与功能相关特征不存在统计依赖性的特征。我们证明局部Shapley值与LIME均违背了这一基本原则。为解决该问题，我们提出R-LOCO（区域排除协变量法），该方法在局部解释与全局解释间架起桥梁，提供更精确的归因结果。R-LOCO将输入空间划分为具有相似特征重要性特性的区域，随后在这些区域内部应用全局归因方法，通过实例所属区域推导其特征贡献。该方法在避免局部解释不稳定性、保留全局方法常丢失的实例特异性细节的同时，提供了更可靠的局部归因。