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Feature relevance quantification in explainable AI: A causal problem.

, , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2907-2916. PMLR, (2020)

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Estimation of interventional effects of features on prediction., and . MLSP, page 1-6. IEEE, (2017)Manifold Restricted Interventional Shapley Values., , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 5079-5106. PMLR, (2023)Causal structure-based root cause analysis of outliers., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 2357-2369. PMLR, (2022)Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies., , , , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 10534-10554. PMLR, (2022)Unsupervised Dimensionality Reduction for Transfer Learning., , and . ESANN, (2015)The PetShop Dataset - Finding Causes of Performance Issues across Microservices., , , , and . CoRR, (2023)The PetShop Dataset - Finding Causes of Performance Issues across Microservices., , , , and . CLeaR, volume 236 of Proceedings of Machine Learning Research, page 957-978. PMLR, (2024)Root Cause Analysis of Outliers with Missing Structural Knowledge., , , , and . CoRR, (2024)Feature relevance quantification in explainable AI: A causal problem., , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2907-2916. PMLR, (2020)Why did the distribution change?, , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 1666-1674. PMLR, (2021)