LUCID：基于条件密度估计的局部变量重要性新方法
LUCID: Local Understanding of Covariate Importance with Conditional Density Estimation
做模型可解释性的人看看这篇：LUCID 用条件密度估计来做局部变量重要性，比普通扰动法更尊重特征依赖，还有收敛保证。
论文提出 LUCID，一种模型无关的局部变量重要性（VI）度量与估计器。其核心是用条件密度估计，从给定其余观测特征后的完整条件分布中抽取合理值来扰动目标特征。相比现有扰动策略，LUCID 避免了生成不符合特征依赖结构的反事实样本。论文还建立了 LUCID 与既有 VI 度量的理论联系，给出了带收敛保证和不确定性量化的估计器，并在模拟与真实数据上验证了稳定表现。
LUCID: Local Understanding of Covariate Importance with Conditional Density Estimation
Computing local-level variable importance (VI) is critical for understanding machine learning models. Local permutation-based VI metrics offer a simple yet powerful approach, measuring how much a model's output changes when perturbing its inputs. However, existing perturbation strategies often ignore feature dependence or account for it only through restricted approximations. Perturbing a feature without fully respecting this dependence can generate unrealistic counterfactuals, thereby evaluating the model in regions unsupported by the observed data distribution. We address this gap by introducing a new model-agnostic local VI metric and estimator, Local Understanding of Covariate Importance with conditional Density estimation (LUCID). LUCID perturbs features of interest using plausible values drawn from the full conditional distribution given the remaining observed features. We establish theoretical connections between LUCID and existing VI measures, and provide an estimator with convergence guarantees and uncertainty quantification. Finally, we demonstrate LUCID's stable performance and practical utility across simulated and real-world examples.
