VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution
This addresses a key limitation in explainable AI for practitioners needing reliable local model interpretations, though it is incremental as it builds on the Shapley value framework.
The paper tackles the problem of global dependency in feature attribution methods like SHAP by introducing VARSHAP, a model-agnostic local method that uses prediction variance reduction as an importance metric, and it outperforms KernelSHAP and LIME on synthetic and real-world datasets.
Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnostic local feature attribution method which uses the reduction of prediction variance as the key importance metric of features. Building upon Shapley value framework, VARSHAP satisfies the key Shapley axioms, but, unlike SHAP, is resilient to global data distribution shifts. Experiments on synthetic and real-world datasets demonstrate that VARSHAP outperforms popular methods such as KernelSHAP or LIME, both quantitatively and qualitatively.