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Quantifying intrinsic causal contributions via structure preserving interventions., , , , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 2188-2196. PMLR, (2024)Causal structure based root cause analysis of outliers., , , and . CoRR, (2019)Causal forecasting: generalization bounds for autoregressive models., , , , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 2002-2012. PMLR, (2022)Correcting Confounding via Random Selection of Background Variables., , and . CoRR, (2022)Unsupervised Model Selection for Time-series Anomaly Detection., , , , and . CoRR, (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)Feature relevance quantification in explainable AI: A causal problem., , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2907-2916. PMLR, (2020)Causal Forecasting: Generalization Bounds for Autoregressive Models., , , , and . CoRR, (2021)Unsupervised Model Selection for Time Series Anomaly Detection., , , , and . ICLR, OpenReview.net, (2023)Quantifying causal contribution via structure preserving interventions., , and . CoRR, (2020)