{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:50:27Z","timestamp":1773481827473,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["389792660"],"award-info":[{"award-number":["389792660"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3630106.3659003","type":"proceedings-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T13:14:21Z","timestamp":1717593261000},"page":"1745-1762","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["CARMA: A practical framework to generate recommendations for causal algorithmic recourse at scale"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7535-6850","authenticated-orcid":false,"given":"Ayan","family":"Majumdar","sequence":"first","affiliation":[{"name":"MPI-SWS, Germany and Saarland University, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6440-4376","authenticated-orcid":false,"given":"Isabel","family":"Valera","sequence":"additional","affiliation":[{"name":"Saarland University, Germany and MPI-SWS, Germany"}]}],"member":"320","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-44064-9_16"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR, 5324\u20135342","author":"Dominguez-Olmedo Ricardo","year":"2022","unstructured":"Ricardo Dominguez-Olmedo, Amir\u00a0H Karimi, and Bernhard Sch\u00f6lkopf. 2022. On the adversarial robustness of causal algorithmic recourse. In International Conference on Machine Learning. PMLR, 5324\u20135342."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3617694.3623251"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 10727\u201310743","author":"Gao Ruijiang","year":"2023","unstructured":"Ruijiang Gao and Himabindu Lakkaraju. 2023. On the impact of algorithmic recourse on social segregation. In International Conference on Machine Learning. PMLR, 10727\u201310743."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2019.2957223"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599290"},{"key":"e_1_3_2_1_8_1","volume-title":"Equalizing recourse across groups. arXiv preprint arXiv:1909.03166","author":"Gupta Vivek","year":"2019","unstructured":"Vivek Gupta, Pegah Nokhiz, Chitradeep\u00a0Dutta Roy, and Suresh Venkatasubramanian. 2019. Equalizing recourse across groups. arXiv preprint arXiv:1909.03166 (2019)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2840728.2840730"},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning. PMLR, 2692\u20132701","author":"Heidari Hoda","year":"2019","unstructured":"Hoda Heidari, Vedant Nanda, and Krishna Gummadi. 2019. On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning. In International Conference on Machine Learning. PMLR, 2692\u20132701."},{"key":"e_1_3_2_1_11_1","volume-title":"Conversational processes and causal explanation.Psychological Bulletin 107, 1","author":"Hilton J","year":"1990","unstructured":"Denis\u00a0J Hilton. 1990. Conversational processes and causal explanation.Psychological Bulletin 107, 1 (1990), 65."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.24432\/C5NC77"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i9.21188"},{"key":"e_1_3_2_1_14_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=rkE3y85ee"},{"key":"e_1_3_2_1_15_1","volume-title":"Causal normalizing flows: from theory to practice. Advances in Neural Information Processing Systems 36","author":"Javaloy Adri\u00e1n","year":"2024","unstructured":"Adri\u00e1n Javaloy, Pablo S\u00e1nchez-Mart\u00edn, and Isabel Valera. 2024. Causal normalizing flows: from theory to practice. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 895\u2013905","author":"Karimi Amir-Hossein","year":"2020","unstructured":"Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera. 2020. Model-agnostic counterfactual explanations for consequential decisions. In International Conference on Artificial Intelligence and Statistics. PMLR, 895\u2013905."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3527848"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445899"},{"key":"e_1_3_2_1_19_1","first-page":"265","article-title":"Algorithmic recourse under imperfect causal knowledge: a probabilistic approach","volume":"33","author":"Karimi Amir-Hossein","year":"2020","unstructured":"Amir-Hossein Karimi, Julius Von\u00a0K\u00fcgelgen, Bernhard Sch\u00f6lkopf, and Isabel Valera. 2020. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Advances in Neural Information Processing Systems 33 (2020), 265\u2013277.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), Vol.\u00a024","author":"Khemakhem Ilyes","year":"2021","unstructured":"Ilyes Khemakhem, Ricardo Monti, Robert Leech, and Aapo Hyvarinen. 2021. Causal autoregressive flows. In Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), Vol.\u00a024. PMLR."},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0108)","author":"Kilbertus Niki","year":"2020","unstructured":"Niki Kilbertus, Manuel\u00a0Gomez Rodriguez, Bernhard Sch\u00f6lkopf, Krikamol Muandet, and Isabel Valera. 2020. Fair Decisions Despite Imperfect Predictions. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0108), Silvia Chiappa and Roberto Calandra (Eds.). PMLR, 277\u2013287."},{"key":"e_1_3_2_1_22_1","volume-title":"Kingma and Max Welling","author":"P.","year":"2014","unstructured":"Diederik\u00a0P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i10.26398"},{"key":"e_1_3_2_1_24_1","volume-title":"Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277","author":"Mahajan Divyat","year":"2019","unstructured":"Divyat Mahajan, Chenhao Tan, and Amit Sharma. 2019. Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462587"},{"key":"e_1_3_2_1_26_1","volume-title":"Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267","author":"Miller Tim","year":"2019","unstructured":"Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 (2019), 1\u201338."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_2_1_28_1","unstructured":"Daniel Nemirovsky Nicolas Thiebaut Ye Xu and Abhishek Gupta. 2022. CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual GANs. In Uncertainty in Artificial Intelligence. PMLR 1488\u20131497."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380087"},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 9680\u20139696","author":"Pawelczyk Martin","year":"2023","unstructured":"Martin Pawelczyk, Himabindu Lakkaraju, and Seth Neel. 2023. On the privacy risks of algorithmic recourse. In International Conference on Artificial Intelligence and Statistics. PMLR, 9680\u20139696."},{"key":"e_1_3_2_1_31_1","volume-title":"Causal inference in statistics: An overview. Statistics surveys 3","author":"Pearl Judea","year":"2009","unstructured":"Judea Pearl. 2009. Causal inference in statistics: An overview. Statistics surveys 3 (2009)."},{"key":"e_1_3_2_1_32_1","volume-title":"Elements of causal inference: foundations and learning algorithms","author":"Peters Jonas","unstructured":"Jonas Peters, Dominik Janzing, and Bernhard Sch\u00f6lkopf. 2017. Elements of causal inference: foundations and learning algorithms. The MIT Press."},{"key":"e_1_3_2_1_33_1","unstructured":"John Platt and Alan Barr. 1987. Constrained differential optimization. In Neural Information Processing Systems."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533199"},{"key":"e_1_3_2_1_35_1","volume-title":"Algorithmic recourse in the wild: Understanding the impact of data and model shifts. arXiv preprint arXiv:2012.11788","author":"Rawal Kaivalya","year":"2020","unstructured":"Kaivalya Rawal, Ece Kamar, and Himabindu Lakkaraju. 2020. Algorithmic recourse in the wild: Understanding the impact of data and model shifts. arXiv preprint arXiv:2012.11788 (2020)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-011-9096-6"},{"key":"e_1_3_2_1_37_1","volume-title":"Are loss functions all the same?Neural computation 16, 5","author":"Rosasco Lorenzo","year":"2004","unstructured":"Lorenzo Rosasco, Ernesto De\u00a0Vito, Andrea Caponnetto, Michele Piana, and Alessandro Verri. 2004. Are loss functions all the same?Neural computation 16, 5 (2004), 1063\u20131076."},{"key":"e_1_3_2_1_38_1","volume-title":"Causal Learning and Reasoning","author":"Sanchez Pedro","year":"2022","unstructured":"Pedro Sanchez and Sotirios\u00a0A Tsaftaris. 2022. Diffusion Causal Models for Counterfactual Estimation. In Causal Learning and Reasoning 2022."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20789"},{"key":"e_1_3_2_1_40_1","unstructured":"Kumba Sennaar. 2019. Machine Learning for Recruiting and Hiring\u20136 Current Applications. En l\u00ednea]. Disponible en: https:\/\/emerj. com\/ai-sector-overviews\/machine-learning-forrecruiting-and-hiring\/.[\u00daltimo acceso: 30 Mayo 2019] (2019)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375812"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Kacper Sokol and Peter\u00a0A Flach. 2018. Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements.. In IJCAI. 5785\u20135786.","DOI":"10.24963\/ijcai.2018\/836"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514094.3534185"},{"key":"e_1_3_2_1_44_1","first-page":"16926","article-title":"Towards robust and reliable algorithmic recourse","volume":"34","author":"Upadhyay Sohini","year":"2021","unstructured":"Sohini Upadhyay, Shalmali Joshi, and Himabindu Lakkaraju. 2021. Towards robust and reliable algorithmic recourse. Advances in Neural Information Processing Systems 34 (2021), 16926\u201316937.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_40"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372876"},{"key":"e_1_3_2_1_48_1","volume-title":"Counterfactual explanations and algorithmic recourses for machine learning: A review. arXiv preprint arXiv:2010.10596","author":"Verma Sahil","year":"2020","unstructured":"Sahil Verma, Varich Boonsanong, Minh Hoang, Keegan\u00a0E Hines, John\u00a0P Dickerson, and Chirag Shah. 2020. Counterfactual explanations and algorithmic recourses for machine learning: A review. arXiv preprint arXiv:2010.10596 (2020)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20828"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-021-00477-0"},{"key":"e_1_3_2_1_51_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a036","author":"von K\u00fcgelgen Julius","year":"2022","unstructured":"Julius von K\u00fcgelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Sch\u00f6lkopf. 2022. On the fairness of causal algorithmic recourse. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol.\u00a036. 9584\u20139594."},{"key":"e_1_3_2_1_52_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v35i1.2504"},{"key":"e_1_3_2_1_54_1","volume-title":"Challenges for Transparency. CoRR abs\/1708.01870","author":"Weller Adrian","year":"2017","unstructured":"Adrian Weller. 2017. Challenges for Transparency. CoRR abs\/1708.01870 (2017). arXiv:1708.01870http:\/\/arxiv.org\/abs\/1708.01870"},{"key":"e_1_3_2_1_55_1","volume-title":"Relating graph neural networks to structural causal models. arXiv preprint arXiv:2109.04173","author":"Ze\u010devi\u0107 Matej","year":"2021","unstructured":"Matej Ze\u010devi\u0107, Devendra\u00a0Singh Dhami, Petar Veli\u010dkovi\u0107, and Kristian Kersting. 2021. Relating graph neural networks to structural causal models. arXiv preprint arXiv:2109.04173 (2021)."}],"event":{"name":"FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency","location":"Rio de Janeiro Brazil","acronym":"FAccT '24"},"container-title":["The 2024 ACM Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3659003","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630106.3659003","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:57:07Z","timestamp":1750291027000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3659003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":55,"alternative-id":["10.1145\/3630106.3659003","10.1145\/3630106"],"URL":"https:\/\/doi.org\/10.1145\/3630106.3659003","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-06-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}