{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T23:01:15Z","timestamp":1754262075144,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China award","award":["62372459,62276273,62425206,62141607"],"award-info":[{"award-number":["62372459,62276273,62425206,62141607"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714859","type":"proceedings-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T16:42:02Z","timestamp":1746463322000},"page":"4507-4518","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning Feasible Causal Algorithmic Recourse: A Prior Structural Knowledge Free Approach"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2928-5575","authenticated-orcid":false,"given":"Haotian","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6000-6936","authenticated-orcid":false,"given":"Hao","family":"Zou","sequence":"additional","affiliation":[{"name":"ZGC laboratory, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9599-4114","authenticated-orcid":false,"given":"Xueguang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Naval University of Engineering, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1469-2063","authenticated-orcid":false,"given":"Shangwen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6997-0406","authenticated-orcid":false,"given":"Wenjing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2957-8511","authenticated-orcid":false,"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Consistent counterfactuals for deep models. arXiv preprint arXiv:2110.03109","author":"Black Emily","year":"2021","unstructured":"Emily Black, Zifan Wang, Matt Fredrikson, and Anupam Datta. 2021. Consistent counterfactuals for deep models. arXiv preprint arXiv:2110.03109 (2021)."},{"key":"e_1_3_2_1_2_1","volume-title":"An improvement of the convergence proof of the ADAM-Optimizer. arXiv preprint arXiv:1804.10587","author":"Bock Sebastian","year":"2018","unstructured":"Sebastian Bock, Josef Goppold, and Martin Wei\u00df. 2018. An improvement of the convergence proof of the ADAM-Optimizer. arXiv preprint arXiv:1804.10587 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"Adversarial patch. arXiv preprint arXiv:1712.09665","author":"Brown Tom B","year":"2017","unstructured":"Tom B Brown, Dandelion Man\u00e9, Aurko Roy, Mart\u00edn Abadi, and Justin Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 (2017)."},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. PMLR, 2357--2369","author":"Budhathoki Kailash","year":"2022","unstructured":"Kailash Budhathoki, Lenon Minorics, Patrick Bl\u00f6baum, and Dominik Janzing. 2022. Causal structure-based root cause analysis of outliers. In International Conference on Machine Learning. PMLR, 2357--2369."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01858-x"},{"key":"e_1_3_2_1_6_1","volume-title":"Conference on Causal Learning and Reasoning. PMLR, 407--427","author":"Kroon Arnoud De","year":"2022","unstructured":"Arnoud De Kroon, Joris Mooij, and Danielle Belgrave. 2022. Causal bandits without prior knowledge using separating sets. In Conference on Causal Learning and Reasoning. PMLR, 407--427."},{"key":"e_1_3_2_1_7_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Dhurandhar Amit","year":"2018","unstructured":"Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_8_1","volume-title":"Review of causal discovery methods based on graphical models. Frontiers in genetics","author":"Glymour Clark","year":"2019","unstructured":"Clark Glymour, Kun Zhang, and Peter Spirtes. 2019. Review of causal discovery methods based on graphical models. Frontiers in genetics, Vol. 10 (2019), 524."},{"key":"e_1_3_2_1_9_1","first-page":"28233","article-title":"Independent mechanism analysis, a new concept","volume":"34","author":"Gresele Luigi","year":"2021","unstructured":"Luigi Gresele, Julius Von K\u00fcgelgen, Vincent Stimper, Bernhard Sch\u00f6lkopf, and Michel Besserve. 2021. Independent mechanism analysis, a new concept? Advances in Neural Information Processing Systems, Vol. 34 (2021), 28233--28248.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_10_1","article-title":"Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics","volume":"13","author":"Gutmann Michael U","year":"2012","unstructured":"Michael U Gutmann and Aapo Hyv\u00e4rinen. 2012. Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics. Journal of machine learning research, Vol. 13, 2 (2012).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_11_1","volume-title":"The cocktail party problem. Neural computation","author":"Haykin Simon","year":"2005","unstructured":"Simon Haykin and Zhe Chen. 2005. The cocktail party problem. Neural computation, Vol. 17, 9 (2005), 1875--1902."},{"key":"e_1_3_2_1_12_1","unstructured":"Aapo Hyvarinen and Hiroshi Morioka. 2017. Nonlinear ICA of temporally dependent stationary sources. In Artificial Intelligence and Statistics. PMLR 460--469."},{"key":"e_1_3_2_1_13_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 859--868","author":"Hyvarinen Aapo","year":"2019","unstructured":"Aapo Hyvarinen, Hiroaki Sasaki, and Richard Turner. 2019. Nonlinear ICA using auxiliary variables and generalized contrastive learning. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 859--868."},{"key":"e_1_3_2_1_14_1","volume-title":"Quantifying causal contribution via structure preserving interventions. arXiv preprint arXiv:2007.00714","author":"Janzing Dominik","year":"2020","unstructured":"Dominik Janzing, Patrick Bl\u00f6baum, Lenon Minorics, and P Faller. 2020. Quantifying causal contribution via structure preserving interventions. arXiv preprint arXiv:2007.00714 (2020)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i13.17376"},{"key":"e_1_3_2_1_16_1","volume-title":"A survey of algorithmic recourse: contrastive explanations and consequential recommendations. ACM Computing Surveys (CSUR)","author":"Karimi Amir-Hossein","year":"2021","unstructured":"Amir-Hossein Karimi, Gilles Barthe, Bernhard Sch\u00f6lkopf, and Isabel Valera. 2021a. A survey of algorithmic recourse: contrastive explanations and consequential recommendations. ACM Computing Surveys (CSUR) (2021)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445899"},{"key":"e_1_3_2_1_18_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 K\u00fcgelgen, Bernhard Sch\u00f6lkopf, and Isabel Valera. 2020. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Advances in Neural Information Processing Systems, Vol. 33 (2020), 265--277.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. PMLR, 5501--5510","author":"Kumor Daniel","year":"2020","unstructured":"Daniel Kumor, Carlos Cinelli, and Elias Bareinboim. 2020. Efficient identification in linear structural causal models with auxiliary cutsets. In International Conference on Machine Learning. PMLR, 5501--5510."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383557"},{"key":"e_1_3_2_1_21_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_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29908-8_4"},{"key":"e_1_3_2_1_23_1","volume-title":"An analysis of the use and success of online recruitment methods in the UK. Human resource management journal","author":"Parry Emma","year":"2008","unstructured":"Emma Parry and Shaun Tyson. 2008. An analysis of the use and success of online recruitment methods in the UK. Human resource management journal, Vol. 18, 3 (2008), 257--274."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380087"},{"key":"e_1_3_2_1_25_1","volume-title":"Daniel Coelho de Castro, and Ben Glocker","author":"Pawlowski Nick","year":"2020","unstructured":"Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker. 2020. Deep structural causal models for tractable counterfactual inference. Advances in neural information processing systems, Vol. 33 (2020), 857--869."},{"key":"e_1_3_2_1_26_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_28_1","first-page":"23359","article-title":"Editing a classifier by rewriting its prediction rules","volume":"34","author":"Santurkar Shibani","year":"2021","unstructured":"Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, and Aleksander Madry. 2021. Editing a classifier by rewriting its prediction rules. Advances in Neural Information Processing Systems, Vol. 34 (2021), 23359--23373.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICESC48915.2020.9155614"},{"key":"e_1_3_2_1_30_1","volume-title":"A selective review of negative control methods in epidemiology. Current epidemiology reports","author":"Shi Xu","year":"2020","unstructured":"Xu Shi, Wang Miao, and Eric Tchetgen Tchetgen. 2020. A selective review of negative control methods in epidemiology. Current epidemiology reports, Vol. 7, 4 (2020), 190--202."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.2333\/bhmk.41.65"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the annual symposium on computer application in medical care. American Medical Informatics Association, 261","author":"Smith Jack W","year":"1988","unstructured":"Jack W Smith, James E Everhart, WC Dickson, William C Knowler, and Robert Scott Johannes. 1988. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the annual symposium on computer application in medical care. American Medical Informatics Association, 261."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i9.21192"},{"key":"e_1_3_2_1_35_1","volume-title":"Conference on Causal Learning and Reasoning. PMLR, 177--196","author":"K\u00fcgelgen Julius Von","year":"2023","unstructured":"Julius Von K\u00fcgelgen, Abdirisak Mohamed, and Sander Beckers. 2023. Backtracking counterfactuals. In Conference on Causal Learning and Reasoning. PMLR, 177--196."},{"key":"e_1_3_2_1_36_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., Vol. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-4572-0_206"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.2196\/18585"},{"key":"e_1_3_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Zheng Xun","year":"2018","unstructured":"Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018. Dags with no tears: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_40_1","volume-title":"Causal discovery with reinforcement learning. arXiv preprint arXiv:1906.04477","author":"Zhu Shengyu","year":"2019","unstructured":"Shengyu Zhu, Ignavier Ng, and Zhitang Chen. 2019. Causal discovery with reinforcement learning. arXiv preprint arXiv:1906.04477 (2019)."},{"key":"e_1_3_2_1_41_1","volume-title":"Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach. arXiv preprint arXiv:2401.10632","author":"Zuo Aoqi","year":"2024","unstructured":"Aoqi Zuo, Yiqing Li, Susan Wei, and Mingming Gong. 2024. Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach. arXiv preprint arXiv:2401.10632 (2024)."}],"event":{"name":"WWW '25: The ACM Web Conference 2025","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Sydney NSW Australia","acronym":"WWW '25"},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714859","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714859","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:42Z","timestamp":1750295922000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":41,"alternative-id":["10.1145\/3696410.3714859","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714859","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}