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Surv."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>Causal inference is the idea of cause and effect; this fundamental area of sciences can be applied to problem space associated with Newton\u2019s laws or the devastating COVID-19 pandemic. The cause explains the \u201cwhy,\u201d whereas the effect describes the \u201cwhat.\u201d The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning and artificial intelligence systems have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. 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Causal inference in the presence of latent variables and selection bias. arXiv preprint arXiv:1302.4983 (2013).","journal-title":"arXiv preprint arXiv:1302.4983"},{"key":"e_1_3_2_164_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304652.3304720"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep21691"},{"key":"e_1_3_2_166_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3470792"},{"key":"e_1_3_2_167_2","article-title":"Neural Granger causality","author":"Tank Alex","year":"2018","unstructured":"Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, and Emily Fox. 2018. Neural Granger causality. arXiv preprint arXiv:1802.05842 (2018).","journal-title":"arXiv preprint arXiv:1802.05842"},{"key":"e_1_3_2_168_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_169_2","first-page":"9537","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Tople Shruti","year":"2020","unstructured":"Shruti Tople, Amit Sharma, and Aditya Nori. 2020. Alleviating privacy attacks via causal learning. In Proceedings of the International Conference on Machine Learning. 9537\u20139547."},{"key":"e_1_3_2_170_2","doi-asserted-by":"publisher","DOI":"10.4108\/eai.11-5-2021.169912"},{"key":"e_1_3_2_171_2","unstructured":"Ioannis Tsamardinos Constantin F. Aliferis Alexander R. Statnikov and Er Statnikov. 2003. Algorithms for large scale Markov blanket discovery. In Proceedings of the 16th International Florida Artificial Intelligence Research Society Conference (FLAIRS \u201903). 376\u2013380."},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"issue":"1","key":"e_1_3_2_173_2","article-title":"Targeted maximum likelihood learning","volume":"2","author":"Laan Mark J. Van Der","year":"2006","unstructured":"Mark J. Van Der Laan and Daniel Rubin. 2006. Targeted maximum likelihood learning. International Journal of Biostatistics 2, 1 (2006), Article 11.","journal-title":"International Journal of Biostatistics"},{"key":"e_1_3_2_174_2","article-title":"Causal inference using LLM-guided discovery","author":"Vashishtha Aniket","year":"2023","unstructured":"Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, and Amit Sharma. 2023. 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On the fairness of causal algorithmic recourse. arXiv preprint arXiv:2010.06529 (2020).","journal-title":"arXiv preprint arXiv:2010.06529"},{"key":"e_1_3_2_177_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_2_178_2","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.aje.a009864"},{"key":"e_1_3_2_179_2","article-title":"Permutation-based causal inference algorithms with interventions","volume":"30","author":"Wang Yuhao","year":"2017","unstructured":"Yuhao Wang, Liam Solus, Karren Yang, and Caroline Uhler. 2017. Permutation-based causal inference algorithms with interventions. Advances in Neural Information Processing Systems 30 (2017), 1\u201310.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_180_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2002.1183994"},{"key":"e_1_3_2_181_2","unstructured":"Tailin Wu Thomas Breuel Michael Skuhersky and Jan Kautz. 2020. Nonlinear causal discovery with minimum predictive information regularization. arXiv:2001.01885 (2020)."},{"key":"e_1_3_2_182_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigDIA53151.2021.9619639"},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357864"},{"key":"e_1_3_2_184_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32236-6_51"},{"key":"e_1_3_2_185_2","volume-title":"Proceedings of the 1st International Workshop on Causality in Search and Recommendation","author":"Xu Shuyuan","year":"2021","unstructured":"Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, and Yongfeng Zhang. 2021. Learning causal explanations for recommendation. 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