{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T04:34:45Z","timestamp":1775622885675,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s10489-025-06945-w","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T11:53:35Z","timestamp":1762257215000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing counterfactual explanations with causal inference: integrating DirectLiNGAM and variational autoencoders for constraint-aware generation"],"prefix":"10.1007","volume":"55","author":[{"given":"Dinh","family":"Dinh","sequence":"first","affiliation":[]},{"given":"Thanh-An","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Ngoc-Thao","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Nghia","family":"Le","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4306-6945","authenticated-orcid":false,"given":"Bac","family":"Le","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"issue":"8","key":"6945_CR1","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/electronics8080832","volume":"8","author":"DV Carvalho","year":"2019","unstructured":"Carvalho DV, Pereira EM, Cardoso JS (2019) Machine learning interpretability: a survey on methods and metrics. Electronics 8(8):832. https:\/\/doi.org\/10.3390\/electronics8080832","journal-title":"Electronics"},{"key":"6945_CR2","unstructured":"Molnar C (2019) Interpretable machine learning. A Guide for Making Black Box Models Explainable. https:\/\/christophm.github.io\/interpretable-ml-book"},{"key":"6945_CR3","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: demonstrations, pp 97\u2013101. https:\/\/doi.org\/10.18653\/v1\/N16-3020","DOI":"10.18653\/v1\/N16-3020"},{"key":"6945_CR4","unstructured":"Lundberg SM, Lee S (2017) A unified approach to interpreting model predictions. In: NIPS\u201917 Proceedings of the 31st international conference on neural information processing systems, vol 30, pp 4768\u20134777. https:\/\/dl.acm.org\/doi\/10.5555\/3295222.3295230"},{"key":"6945_CR5","doi-asserted-by":"publisher","unstructured":"Stepin I, Alonso JM, Catala A, Pereira-Farina M (2021) A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access 9:11 974\u201312 001. https:\/\/doi.org\/10.1109\/ACCESS.2021.3051315","DOI":"10.1109\/ACCESS.2021.3051315"},{"key":"6945_CR6","doi-asserted-by":"publisher","unstructured":"Guidotti R (2022) Counterfactual explanations and how to find them: literature review and benchmarking. Data Min Knowl Discov, 1\u201355. https:\/\/doi.org\/10.1007\/s10618-022-00831-6","DOI":"10.1007\/s10618-022-00831-6"},{"key":"6945_CR7","doi-asserted-by":"publisher","unstructured":"Mahajan D, Tan C, Sharma A (2019) Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv: 1912.03277. https:\/\/doi.org\/10.48550\/arXiv.1912.03277","DOI":"10.48550\/arXiv.1912.03277"},{"key":"6945_CR8","doi-asserted-by":"publisher","unstructured":"Karimi A-H, Sch\u00f6lkopf B, Valera I (2020) Algorithmic recourse: from counterfactual explanations to interventions. arXiv:2002.06278. https:\/\/doi.org\/10.48550\/arXiv.2002.06278","DOI":"10.48550\/arXiv.2002.06278"},{"key":"6945_CR9","unstructured":"Downs M, Chu JL, Yacoby Y, Doshi-Velez F, Pan W (2020) CRUDS: counterfactual recourse using disentangled subspaces. In: ICML workshop on human interpretability in machine learning. https:\/\/scholar.harvard.edu\/files\/finale\/files\/cruds-_counterfactual_recourse_using_disentangled_subspaces.pdf"},{"key":"6945_CR10","doi-asserted-by":"publisher","unstructured":"Xiang X, Lenskiy A (2022) Realistic counterfactual explanations by learned relations. arXiv:2202.07356. https:\/\/doi.org\/10.48550\/arXiv.2202.07356","DOI":"10.48550\/arXiv.2202.07356"},{"key":"6945_CR11","doi-asserted-by":"publisher","unstructured":"Albini E, Rago A, Baroni P, Toni F (2020) Relation-based counterfactual explanations for Bayesian network classifiers. In: IJCAI, pp 451\u2013457. https:\/\/doi.org\/10.24963\/ijcai.2020\/63","DOI":"10.24963\/ijcai.2020\/63"},{"key":"6945_CR12","doi-asserted-by":"publisher","unstructured":"Shimizu S, Inazumi T, Sogawa Y, Hyvarinen A, Kawahara Y, Washio T, Hoyer PO, Bollen K (2011) DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model. arXiv:1101.2489. https:\/\/doi.org\/10.48550\/arXiv.1101.2489","DOI":"10.48550\/arXiv.1101.2489"},{"key":"6945_CR13","doi-asserted-by":"publisher","unstructured":"Guidotti R (2022) Counterfactual explanations and how to find them: literature review and benchmarking. Data Min Knowl Disc. https:\/\/doi.org\/10.1007\/s10618-022-00831-6","DOI":"10.1007\/s10618-022-00831-6"},{"key":"6945_CR14","doi-asserted-by":"publisher","unstructured":"Cui Z, Chen W, He Y, Chen Y (2015) Optimal action extraction for random forests and boosted trees. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, NSW, Australia, August 10\u201313, ACM, pp 179\u2013188. https:\/\/doi.org\/10.1145\/2783258.2783281","DOI":"10.1145\/2783258.2783281"},{"key":"6945_CR15","doi-asserted-by":"crossref","unstructured":"Wachter S, Mittelstadt B, Russell C (2017) Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv JL Tech, pp 31, 841. https:\/\/jolt.law.harvard.edu\/assets\/articlePDFs\/v31\/Counterfactual-Explanations-without-Opening-the-Black-Box-Sandra-Wachter-et-al.pdf","DOI":"10.2139\/ssrn.3063289"},{"key":"6945_CR16","unstructured":"Dhurandhar A, Chen P, Luss R, Tu C, Ting P, Shanmugam K, Das P (2018) Explanations based on the missing: towards contrastive explanations with pertinent negatives. In: Advances in neural information processing systems 31: annual conference on neural information processing systems 2018, NeurIPS 2018, December 3\u20138, 2018, Montr\u00e9al, Canada, pp 590\u2013601. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2018\/file\/c5ff2543b53f4cc0ad3819a36752467b-Paper.pdf"},{"key":"6945_CR17","doi-asserted-by":"publisher","unstructured":"Rathi S (2019) Generating counterfactual and contrastive explanations using SHAP. CoRR arXiv:1906.09293. https:\/\/doi.org\/10.48550\/arXiv.1906.09293","DOI":"10.48550\/arXiv.1906.09293"},{"issue":"1","key":"6945_CR18","doi-asserted-by":"publisher","first-page":"73","DOI":"10.25300\/MISQ\/2014\/38.1.04","volume":"38","author":"D Martens","year":"2014","unstructured":"Martens D, Provost FJ (2014) Explaining data-driven document classifications. MIS Q 38(1):73\u201399. https:\/\/doi.org\/10.25300\/MISQ\/2014\/38.1.04","journal-title":"Explaining data-driven document classifications. MIS Q"},{"issue":"2","key":"6945_CR19","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1109\/TNN.2008.917504","volume":"19","author":"G Shakhnarovich","year":"2008","unstructured":"Shakhnarovich G, Darrell T, Indyk P (2008) Nearest-neighbor methods in learning and vision. IEEE Trans Neural Netw 19(2):377","journal-title":"IEEE Trans Neural Netw"},{"key":"6945_CR20","doi-asserted-by":"publisher","unstructured":"Poyiadzi R, Sokol K, Santos-Rodriguez R, De Bie T, Flach P (2020) Face: feasible and actionable counterfactual explanations. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society, pp 344\u2013350. https:\/\/doi.org\/10.1145\/3375627.3375850","DOI":"10.1145\/3375627.3375850"},{"key":"6945_CR21","doi-asserted-by":"publisher","unstructured":"Ahmad S, Wang H (2025) TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation. J Stat Comput Simul, 1\u201329. https:\/\/doi.org\/10.1080\/00949655.2025.2516793","DOI":"10.1080\/00949655.2025.2516793"},{"key":"6945_CR22","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s10489-025-06738-1","volume":"55","author":"S Ahmad","year":"2025","unstructured":"Ahmad S, Wang H (2025) Transformer-variational autoencoder for estimating individual treatment effect using causal inference framework. Appl Intell 55:855. https:\/\/doi.org\/10.1007\/s10489-025-06738-1","journal-title":"Appl Intell"},{"key":"6945_CR23","doi-asserted-by":"publisher","unstructured":"Verma S, Dickerson JP, Hines K (2020) Counterfactual explanations for machine learning: a review. arXiv:2010.10596. https:\/\/doi.org\/10.48550\/arXiv.2010.10596","DOI":"10.48550\/arXiv.2010.10596"},{"key":"6945_CR24","doi-asserted-by":"publisher","unstructured":"Mothilal RK, Sharma A, Tan C (2020) Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 607\u2013617. https:\/\/doi.org\/10.1145\/3351095.3372850","DOI":"10.1145\/3351095.3372850"},{"key":"6945_CR25","doi-asserted-by":"publisher","unstructured":"Leyder S, Raymaekers J, Verdunck T (2023) TSLiNGAM: DirectLiNGAM under heavy tails. arXiv:2308.05422. https:\/\/doi.org\/10.48550\/arXiv.2308.05422","DOI":"10.48550\/arXiv.2308.05422"},{"key":"6945_CR26","doi-asserted-by":"publisher","first-page":"117020","DOI":"10.1016\/j.cam.2025.117020","volume":"475","author":"S Ahmad","year":"2026","unstructured":"Ahmad S, Shah K, Debbouche A (2026) Structural equation modeling for causal effect estimation with machine learning. J Comput Appl Math 475:117020. https:\/\/doi.org\/10.1016\/j.cam.2025.117020","journal-title":"J Comput Appl Math"},{"key":"6945_CR27","unstructured":"Kohavi R, Becker B (1996) Adult. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5XW20"},{"key":"6945_CR28","unstructured":"Bayesian Network Repository (2022). https:\/\/www.bnlearn.com\/bnrepository\/clgaussian-small.html#sangiovese"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06945-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06945-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06945-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T08:04:14Z","timestamp":1764144254000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06945-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":28,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["6945"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06945-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"8 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Informed consent was obtained from all of the subjects involved in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"1074"}}