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Data-Driven Approximation of the Perron-Frobenius Operator Using the Wasserstein Metric., and . CoRR, (2020)Advances in Algorithmic Recourse: Ensuring Causal Consistency, Fairness, & Robustness.. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/642546).Scaling Guarantees for Nearest Counterfactual Explanations., , , and . AIES, page 177-187. ACM, (2021)On the Relationship Between Explanation and Prediction: A Causal View., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 15861-15883. PMLR, (2023)Algorithmic Recourse: from Counterfactual Explanations to Interventions., , and . CoRR, (2020)Robustness Implies Fairness in Causal Algorithmic Recourse., , , and . FAccT, page 984-1001. ACM, (2023)Socially Responsible Machine Learning: A Causal Perspective., , , and . KDD, page 5819-5820. ACM, (2023)On the Fairness of Causal Algorithmic Recourse., , , , , and . AAAI, page 9584-9594. AAAI Press, (2022)Model-Agnostic Counterfactual Explanations for Consequential Decisions., , , and . CoRR, (2019)Distance Correlation Autoencoder., , and . IJCNN, page 1-8. IEEE, (2018)