Researchshapxaitree ensemblesdempster shafer
Researchers Decompose Uncertainty in SHAP Explanations
8.1
A new preprint (v2 posted 24 Feb 2026) proposes a method to decompose uncertainty in SHAP values for tree ensembles, separating aleatoric, epistemic, and entanglement components via Dempster-Shafer evidence theory and Dirichlet-process hypothesis sampling. The authors validate the approach across three real-world use cases and show that high-SHAP features can be unstable, advising data and model improvements.
Key Points
- 1Decompose SHAP uncertainty into aleatoric, epistemic, and entanglement via Dempster-Shafer and Dirichlet sampling
- 2Reveal that epistemic uncertainty makes high-SHAP features potentially unstable across tree-ensemble explanations
- 3Advise practitioners to collect representative data and use bagging to quantify and reduce epistemic uncertainty
Scoring Rationale
Novel methodological contribution with practical validation; limited by preprint status and narrower focus on tree ensembles.
Sources
Public references used for this report.
Practice interview problems based on real data
1,625 SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems