{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:12:01Z","timestamp":1780359121308,"version":"3.54.1"},"reference-count":165,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP200101210"],"award-info":[{"award-number":["DP200101210"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted to address this challenging problem from different angles. For estimating causal effect in observational data, assumptions such as Markov condition, faithfulness, and causal sufficiency are always made. Under the assumptions, full knowledge, such as a set of covariates or an underlying causal graph, is typically required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge. In this survey, we review these data-driven methods on causal effect estimation for a single treatment with a single outcome of interest and focus on the challenges faced by data-driven causal effect estimation. We concisely summarise the basic concepts and theories that are essential for data-driven causal effect estimation using graphical causal modelling but are scattered around the literature. We identify and discuss the challenges faced by data-driven causal effect estimation and characterise the existing methods by their assumptions and the approaches to tackling the challenges. We analyse the strengths and limitations of the different types of methods and present an empirical evaluation to support the discussions. We hope this review will motivate more researchers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation.<\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3636423","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:21:57Z","timestamp":1701865317000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0383-1462","authenticated-orcid":false,"given":"Debo","family":"Cheng","sequence":"first","affiliation":[{"name":"UniSA STEM, University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-1878","authenticated-orcid":false,"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-5738","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0794-0404","authenticated-orcid":false,"given":"Jixue","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9732-4313","authenticated-orcid":false,"given":"Thuc Duy","family":"Le","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(02)00201-4"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA11293"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/645920.672836"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOS626"},{"key":"e_1_3_2_6_2","first-page":"171","article-title":"Local causal and Markov blanket induction for causal discovery and feature selection for classification Part I: Algorithms and empirical evaluation","volume":"11","author":"Aliferis Constantin F.","year":"2010","unstructured":"Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, et\u00a0al. 2010. Local causal and Markov blanket induction for causal discovery and feature selection for classification Part I: Algorithms and empirical evaluation. Journal of Machine Learning Research 11, Jan (2010), 171\u2013234.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/15592294.2015.1029700"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476535"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1996.10476902"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(94)01642-D"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510489113"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12268"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1709"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6128"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2005.00377.x"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.9074"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asx053"},{"key":"e_1_3_2_18_2","first-page":"3564","volume-title":"Advances in Neural Information Processing Systems","author":"Bennett Andrew","year":"2019","unstructured":"Andrew Bennett, Nathan Kallus, and Tobias Schnabel. 2019. Deep generalized method of moments for instrumental variable analysis. In Advances in Neural Information Processing Systems. 3564\u20133574."},{"key":"e_1_3_2_19_2","first-page":"202","volume-title":"Uncertainty in Artificial Intelligence","author":"Bhattacharya Rohit","year":"2022","unstructured":"Rohit Bhattacharya and Razieh Nabi. 2022. On testability of the front-door model via Verma constraints. In Uncertainty in Artificial Intelligence. PMLR, 202\u2013212."},{"key":"e_1_3_2_20_2","first-page":"27","article-title":"Semiparametric inference for causal effects in graphical models with hidden variables","volume":"1050","author":"Bhattacharya Rohit","year":"2020","unstructured":"Rohit Bhattacharya, Razieh Nabi, and Ilya Shpitser. 2020. Semiparametric inference for causal effects in graphical models with hidden variables. Stat 1050 (2020), 27.","journal-title":"Stat"},{"key":"e_1_3_2_21_2","volume-title":"Instrumental Variables","author":"Bowden Roger J.","year":"1990","unstructured":"Roger J. Bowden and Darrell A. Turkington. 1990. Instrumental Variables. Vol. 8. Cambridge University Press."},{"key":"e_1_3_2_22_2","first-page":"85","volume-title":"The Conference on Uncertainty in Artificial Intelligence","author":"Brito Carlos","year":"2002","unstructured":"Carlos Brito and Judea Pearl. 2002. Generalized instrumental variables. In The Conference on Uncertainty in Artificial Intelligence. 85\u201393."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17117-4"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-14156-4"},{"key":"e_1_3_2_25_2","first-page":"1127","volume-title":"Econometrica","author":"Card David","year":"1993","unstructured":"David Card. 1993. Using geographic variation in college proximity to estimate the return to schooling. In Econometrica, Vol. 69. CiteSeer, 1127\u20131160."},{"key":"e_1_3_2_26_2","first-page":"2551","volume-title":"24th European Conference on Artificial Intelligence","author":"Cheng Debo","year":"2020","unstructured":"Debo Cheng, Jiuyong Li, Lin Liu, et\u00a0al. 2020. Causal query in observational data with hidden variables. In 24th European Conference on Artificial Intelligence. 2551\u20132558."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/671"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00832-5"},{"issue":"11","key":"e_1_3_2_29_2","first-page":"1","article-title":"Toward unique and unbiased causal effect estimation from data with hidden variables","volume":"34","author":"Cheng Debo","year":"2022","unstructured":"Debo Cheng, Jiuyong Li, Lin Liu, et\u00a0al. 2022. Toward unique and unbiased causal effect estimation from data with hidden variables. IEEE Transactions on Neural Networks and Learning Systems 34, 11 (2022), 1\u201313.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3300916"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3218131"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/978-1-4612-2404-4_12","volume-title":"Learning from Data","author":"Chickering David Maxwell","year":"1996","unstructured":"David Maxwell Chickering. 1996. Learning Bayesian networks is NP-complete. In Learning from Data. Springer, 121\u2013130."},{"key":"e_1_3_2_33_2","first-page":"445","article-title":"Learning equivalence classes of Bayesian-network structures","volume":"2","author":"Chickering David Maxwell","year":"2002","unstructured":"David Maxwell Chickering. 2002. Learning equivalence classes of Bayesian-network structures. Journal of Machine Learning Research 2, Feb (2002), 445\u2013498.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1111\/0272-4332.00039"},{"key":"e_1_3_2_35_2","first-page":"83","volume-title":"The Conference on Uncertainty in Artificial Intelligence","author":"Chu Tianjiao","year":"2001","unstructured":"Tianjiao Chu, Richard Scheines, and Peter Spirtes. 2001. Semi-instrumental variables: a test for instrument admissibility. In The Conference on Uncertainty in Artificial Intelligence. 83\u201390."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwn164"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1214\/11-AOS940"},{"issue":"1","key":"e_1_3_2_38_2","first-page":"173","article-title":"Smoking and lung cancer: Recent evidence and a discussion of some questions","volume":"22","author":"Cornfield Jerome","year":"1959","unstructured":"Jerome Cornfield, William Haenszel, et\u00a0al. 1959. Smoking and lung cancer: Recent evidence and a discussion of some questions. Journal of the National Cancer Institute 22, 1 (1959), 173\u2013203.","journal-title":"Journal of the National Cancer Institute"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11060"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asr041"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2017.12.005"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0000000000000457"},{"key":"e_1_3_2_43_2","article-title":"An automated approach to causal inference in discrete settings","author":"Duarte Guilherme","year":"2021","unstructured":"Guilherme Duarte, Noam Finkelstein, Dean Knox, Jonathan Mummolo, and Ilya Shpitser. 2021. An automated approach to causal inference in discrete settings. arXiv preprint arXiv:2109.13471 (2021).","journal-title":"arXiv preprint arXiv:2109.13471"},{"key":"e_1_3_2_44_2","first-page":"256","volume-title":"Artificial Intelligence and Statistics","author":"Entner Doris","year":"2013","unstructured":"Doris Entner, Patrik Hoyer, and Peter Spirtes. 2013. Data-driven covariate selection for nonparametric estimation of causal effects. In Artificial Intelligence and Statistics. 256\u2013264."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1206"},{"key":"e_1_3_2_46_2","first-page":"270","volume-title":"Conference on Uncertainty in Artificial Intelligence","author":"Fang Zhuangyan","year":"2020","unstructured":"Zhuangyan Fang and Yangbo He. 2020. IDA with background knowledge. In Conference on Uncertainty in Artificial Intelligence. PMLR, 270\u2013279."},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq439"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00524"},{"key":"e_1_3_2_49_2","volume-title":"Computation, Causation, and Discovery","author":"Glymour Clark N.","year":"1999","unstructured":"Clark N. Glymour and Gregory Floyd Cooper. 1999. Computation, Causation, and Discovery. AAAI Press."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11065-008-9066-x"},{"key":"e_1_3_2_51_2","article-title":"Valid inference after causal discovery","author":"Gradu Paula","year":"2022","unstructured":"Paula Gradu, Tijana Zrnic, Yixin Wang, and Michael I. Jordan. 2022. Valid inference after causal discovery. arXiv preprint arXiv:2208.05949 (2022).","journal-title":"arXiv preprint arXiv:2208.05949"},{"key":"e_1_3_2_52_2","volume-title":"Econometric Analysis","author":"Greene William H.","year":"2003","unstructured":"William H. Greene. 2003. Econometric Analysis. Pearson Education India."},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.EDE.0000042804.12056.6C"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-199901000-00008"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19478-2"},{"issue":"4","key":"e_1_3_2_56_2","first-page":"1","article-title":"A survey of learning causality with data: Problems and methods","volume":"53","author":"Guo Ruocheng","year":"2020","unstructured":"Ruocheng Guo, Lu Cheng, Jundong Li, et\u00a0al. 2020. A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR) 53, 4 (2020), 1\u201337.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12788"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v068.i01"},{"key":"e_1_3_2_59_2","first-page":"1414","volume-title":"International Conference on Machine Learning","author":"Hartford Jason","year":"2017","unstructured":"Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. 2017. Deep IV: A flexible approach for counterfactual prediction. In International Conference on Machine Learning. PMLR, 1414\u20131423."},{"key":"e_1_3_2_60_2","first-page":"4096","volume-title":"International Conference on Machine Learning","author":"Hartford Jason S.","year":"2021","unstructured":"Jason S. Hartford, Victor Veitch, Dhanya Sridhar, and Kevin Leyton-Brown. 2021. Valid causal inference with (some) invalid instruments. In International Conference on Machine Learning. PMLR, 4096\u20134106."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1751-5823.2007.00024.x"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12451"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000222409.00878.37"},{"key":"e_1_3_2_64_2","volume-title":"Causal Inference, What If","author":"Hern\u00e1n Miguel A.","year":"2020","unstructured":"Miguel A. Hern\u00e1n and James M. Robins. 2020. Causal Inference, What If. CRC, Boca Raton, FL."},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00442"},{"key":"e_1_3_2_67_2","first-page":"340","volume-title":"The Conference on Uncertainty in Artificial Intelligence","author":"Hyttinen Antti","year":"2014","unstructured":"Antti Hyttinen, Frederick Eberhardt, and Matti J\u00e4rvisalo. 2014. Constraint-based causal discovery: Conflict resolution with answer set programming. In The Conference on Uncertainty in Artificial Intelligence. 340\u2013349."},{"key":"e_1_3_2_68_2","first-page":"395","volume-title":"The Conference on Uncertainty in Artificial Intelligence","author":"Hyttinen Antti","year":"2015","unstructured":"Antti Hyttinen, Frederick Eberhardt, and Matti J\u00e4rvisalo. 2015. Do-calculus when the true graph is unknown. In The Conference on Uncertainty in Artificial Intelligence. CiteSeer, 395\u2013404."},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12027"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1214\/14-STS480"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1257\/jel.20191597"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751"},{"key":"e_1_3_2_73_2","article-title":"Identification of conditional causal effects under Markov equivalence","volume":"32","author":"Jaber Amin","year":"2019","unstructured":"Amin Jaber, Jiji Zhang, and Elias Bareinboim. 2019. Identification of conditional causal effects under Markov equivalence. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/859"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v047.i11"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.994705"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.5555\/1795555"},{"key":"e_1_3_2_78_2","first-page":"190","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Kuroki Manabu","year":"2005","unstructured":"Manabu Kuroki and Zhihong Cai. 2005. Instrumental variable tests for directed acyclic graph models. In International Conference on Artificial Intelligence and Statistics. 190\u2013197."},{"issue":"4","key":"e_1_3_2_79_2","first-page":"604","article-title":"Evaluating the econometric evaluations of training programs with experimental data","volume":"76","author":"LaLonde Robert J.","year":"1986","unstructured":"Robert J. LaLonde. 1986. Evaluating the econometric evaluations of training programs with experimental data. The American Economic Review 76, 4 (1986), 604\u2013620.","journal-title":"The American Economic Review"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt048"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0140-2"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295391"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/384"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1295"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth0410-247"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS685"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2017.06.005"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000215160.88317.cb"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12136"},{"key":"e_1_3_2_90_2","first-page":"403","volume-title":"The 11th Conference on Uncertainty in Artificial Intelligence","author":"Meek Christopher","year":"1995","unstructured":"Christopher Meek. 1995. Causal inference and causal explanation with background knowledge. In The 11th Conference on Uncertainty in Artificial Intelligence. 403\u2013411."},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1177\/0049124106289164"},{"key":"e_1_3_2_92_2","volume-title":"Counterfactuals and Causal Inference","author":"Morgan Stephen L.","year":"2015","unstructured":"Stephen L. Morgan and Christopher Winship. 2015. Counterfactuals and Causal Inference. Cambridge University Press."},{"key":"e_1_3_2_93_2","first-page":"1445","volume-title":"Uncertainty in Artificial Intelligence","author":"Nabi Razieh","year":"2022","unstructured":"Razieh Nabi, Todd McNutt, and Ilya Shpitser. 2022. Semiparametric causal sufficient dimension reduction of multidimensional treatments. In Uncertainty in Artificial Intelligence. PMLR, 1445\u20131455."},{"key":"e_1_3_2_94_2","volume-title":"Learning Bayesian Networks","author":"Neapolitan Richard E.","year":"2004","unstructured":"Richard E. Neapolitan et\u00a0al. 2004. Learning Bayesian Networks. Vol. 38. Pearson Prentice Hall, Upper Saddle River, NJ."},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1449"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/82.4.669"},{"key":"e_1_3_2_97_2","first-page":"435","volume-title":"The Conference on Uncertainty in Artificial Intelligence","author":"Pearl Judea","year":"1995","unstructured":"Judea Pearl. 1995. On the testability of causal models with latent and instrumental variables. In The Conference on Uncertainty in Artificial Intelligence. 435\u2013443."},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_3_2_99_2","article-title":"Myth, confusion, and science in causal analysis","author":"Pearl Judea","year":"2009","unstructured":"Judea Pearl. 2009. Myth, confusion, and science in causal analysis. Technical Report R-348 (2009). Los Angeles, CA: University of California.","journal-title":"Technical Report R-348"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS057"},{"key":"e_1_3_2_101_2","volume-title":"The Book of Why: The New Science of Cause and Effect","author":"Pearl Judea","year":"2018","unstructured":"Judea Pearl and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic Books."},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41237-018-0051-2"},{"key":"e_1_3_2_103_2","first-page":"ID\u2013120","volume-title":"The Conference on Uncertainty in Artificial Intelligence","author":"Perkovic Emilija","year":"2017","unstructured":"Emilija Perkovic, Markus Kalisch, and Marloes H. Maathuis. 2017. Interpreting and using CPDAGs with background knowledge. In The Conference on Uncertainty in Artificial Intelligence. AUAI Press, ID\u2013120."},{"issue":"1","key":"e_1_3_2_104_2","first-page":"8132","article-title":"Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs","volume":"18","author":"Perkovi\u0107 Emilija","year":"2018","unstructured":"Emilija Perkovi\u0107, Johannes Textor, Markus Kalisch, and Marloes H. Maathuis. 2018. Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs. The Journal of Machine Learning Research 18, 1 (2018), 8132\u20138193.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.5555\/3202377"},{"key":"e_1_3_2_106_2","first-page":"1","article-title":"Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects","author":"Ren Shangsi","year":"2023","unstructured":"Shangsi Ren, Cameron A. Beeche, et\u00a0al. 2023. Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects. Medical Physics (2023), 1\u201310.","journal-title":"Medical Physics"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9469.00323"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1031689015"},{"key":"e_1_3_2_109_2","first-page":"83","article-title":"Causal inference via ancestral graph models","volume":"27","author":"Richardson Thomas S.","year":"2003","unstructured":"Thomas S. Richardson and Peter Spirtes. 2003. Causal inference via ancestral graph models. Oxford Statistical Science Series 27 (2003), 83\u2013105.","journal-title":"Oxford Statistical Science Series"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1016\/0270-0255(86)90088-6"},{"key":"e_1_3_2_111_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/978-1-4612-1842-5_4","volume-title":"Latent Variable Modeling and Applications to Causality","author":"Robins James M.","year":"1997","unstructured":"James M. Robins. 1997. Causal inference from complex longitudinal data. In Latent Variable Modeling and Applications to Causality. Springer, 69\u2013117."},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-199203000-00013"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-200009000-00011"},{"key":"e_1_3_2_114_2","doi-asserted-by":"crossref","unstructured":"James M. Robins Andrea Rotnitzky and Daniel O. Scharfstein. 2000. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. 116 (2000) 1\u201394.","DOI":"10.1007\/978-1-4612-1284-3_1"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.2307\/2530647"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455904"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.2307\/2529684"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2739"},{"key":"e_1_3_2_120_2","first-page":"15762","article-title":"Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables","volume":"34","author":"Runge Jakob","year":"2021","unstructured":"Jakob Runge. 2021. Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables. Advances in Neural Information Processing Systems 34 (2021), 15762\u201315773.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10105-3"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1105809"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1002\/pds.3506"},{"key":"e_1_3_2_124_2","article-title":"Semiparametric sensitivity analysis: Unmeasured confounding in observational studies","author":"Scharfstein Daniel O.","year":"2021","unstructured":"Daniel O. Scharfstein, Razieh Nabi, Edward H. Kennedy, Ming-Yueh Huang, Matteo Bonvini, and Marcela Smid. 2021. Semiparametric sensitivity analysis: Unmeasured confounding in observational studies. arXiv preprint arXiv:2104.08300 (2021).","journal-title":"arXiv preprint arXiv:2104.08300"},{"key":"e_1_3_2_125_2","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1145\/3501714.3501755","volume-title":"Probabilistic and Causal Inference: The Works of Judea Pearl","author":"Sch\u00f6lkopf Bernhard","year":"2022","unstructured":"Bernhard Sch\u00f6lkopf. 2022. Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl. 765\u2013804."},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v035.i03"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v042.i07"},{"key":"e_1_3_2_128_2","first-page":"3076","volume-title":"International Conference on Machine Learning","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: Generalization bounds and algorithms. In International Conference on Machine Learning. PMLR, 3076\u20133085."},{"key":"e_1_3_2_129_2","first-page":"1219","volume-title":"Proceedings of the National Conference on Artificial Intelligence","volume":"21","author":"Shpitser Ilya","year":"2006","unstructured":"Ilya Shpitser and Judea Pearl. 2006. Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the National Conference on Artificial Intelligence, Vol. 21. 1219\u20131226."},{"key":"e_1_3_2_130_2","first-page":"1941","article-title":"Complete identification methods for the causal hierarchy","volume":"9","author":"Shpitser Ilya","year":"2008","unstructured":"Ilya Shpitser and Judea Pearl. 2008. Complete identification methods for the causal hierarchy. Journal of Machine Learning Research 9, Sep (2008), 1941\u20131979.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_131_2","first-page":"527","volume-title":"The 26th Conference on Uncertainty in Artificial Intelligence","author":"Shpitser Ilya","year":"2010","unstructured":"Ilya Shpitser, Tyler VanderWeele, and James M. Robins. 2010. On the validity of covariate adjustment for estimating causal effects. In The 26th Conference on Uncertainty in Artificial Intelligence. 527\u2013536."},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.22.00080"},{"key":"e_1_3_2_133_2","first-page":"670","volume-title":"Proceedings of the 14th International Conference on Artificial Intelligence and Statistics","author":"Silva Ricardo","year":"2011","unstructured":"Ricardo Silva, Charles Blundell, and Yee Whye Teh. 2011. Mixed cumulative distribution networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 670\u2013678."},{"issue":"120","key":"e_1_3_2_134_2","first-page":"1","article-title":"Learning instrumental variables with structural and non-Gaussianity assumptions","volume":"18","author":"Silva Ricardo","year":"2017","unstructured":"Ricardo Silva and Shohei Shimizu. 2017. Learning instrumental variables with structural and non-Gaussianity assumptions. Journal of Machine Learning Research 18, 120 (2017), 1\u201349.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_135_2","first-page":"4593","volume-title":"International Conference on Neural Information Processing Systems","author":"Singh Rahul","year":"2019","unstructured":"Rahul Singh, Maneesh Sahani, and Arthur Gretton. 2019. Kernel instrumental variable regression. In International Conference on Neural Information Processing Systems. 4593\u20134605."},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1515\/em-2018-0024"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOAS1316"},{"issue":"5","key":"e_1_3_2_138_2","first-page":"1643","article-title":"Introduction to causal inference.","volume":"11","author":"Spirtes Peter","year":"2010","unstructured":"Peter Spirtes. 2010. Introduction to causal inference. Journal of Machine Learning Research 11, 5 (2010), 1643\u20131662.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_139_2","volume-title":"Causation, Prediction, and Search","author":"Spirtes Peter","year":"2000","unstructured":"Peter Spirtes, Clark N. Glymour, Richard Scheines, et\u00a0al. 2000. Causation, Prediction, and Search. MIT Press."},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1214\/09-STS313"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.18632\/aging.203121"},{"issue":"6","key":"e_1_3_2_142_2","first-page":"1887","article-title":"Robust causal inference using directed acyclic graphs: The R package \u2019dagitty\u2019","volume":"45","author":"Textor Johannes","year":"2016","unstructured":"Johannes Textor, Benito van der Zander, Mark S. Gilthorpe, et\u00a0al. 2016. Robust causal inference using directed acyclic graphs: The R package \u2019dagitty\u2019. International Journal of Epidemiology 45, 6 (2016), 1887\u20131894.","journal-title":"International Journal of Epidemiology"},{"key":"e_1_3_2_143_2","unstructured":"Jin Tian and Judea Pearl. 2002. A general identification condition for causal effects. In AAAI\/IAAI. 567\u2013573."},{"key":"e_1_3_2_144_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1080\/174159794088027573"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"key":"e_1_3_2_147_2","first-page":"907","volume-title":"The 13th Conference on Uncertainty in Artificial Intelligence","author":"Zander Benito van der","year":"2014","unstructured":"Benito van der Zander, Maciej Li\u015bkiewicz, and Johannes Textor. 2014. Constructing separators and adjustment sets in ancestral graphs. In The 13th Conference on Uncertainty in Artificial Intelligence. 907\u2013916."},{"key":"e_1_3_2_148_2","unstructured":"Benito Van der Zander Maciej Li\u015bkiewicz and Johannes Textor. 2015. Efficiently finding conditional instruments for causal inference. (2015) 3243\u20133249."},{"key":"e_1_3_2_149_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.12.006"},{"key":"e_1_3_2_150_2","doi-asserted-by":"publisher","DOI":"10.7326\/M16-2607"},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2011.01619.x"},{"key":"e_1_3_2_152_2","volume-title":"A Guide to Modern Econometrics","author":"Verbeek Marno","year":"2008","unstructured":"Marno Verbeek. 2008. A Guide to Modern Econometrics. John Wiley & Sons."},{"key":"e_1_3_2_153_2","doi-asserted-by":"publisher","DOI":"10.1145\/3527154"},{"key":"e_1_3_2_154_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1686987"},{"key":"e_1_3_2_155_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201700294"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455962"},{"key":"e_1_3_2_157_2","article-title":"Instrumental variables in causal inference and machine learning: A survey","author":"Wu Anpeng","year":"2022","unstructured":"Anpeng Wu, Kun Kuang, Ruoxuan Xiong, and Fei Wu. 2022. Instrumental variables in causal inference and machine learning: A survey. arXiv preprint arXiv:2212.05778 (2022).","journal-title":"arXiv preprint arXiv:2212.05778"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1145\/3444944"},{"key":"e_1_3_2_159_2","first-page":"2638","volume-title":"Advances in Neural Information Processing Systems","author":"Yao Liuyi","year":"2018","unstructured":"Liuyi Yao, Sheng Li, Yaliang Li, et\u00a0al. 2018. Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems. 2638\u20132648."},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.1145\/3436891"},{"issue":"4","key":"e_1_3_2_161_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3494568","article-title":"Auto IV: Counterfactual prediction via automatic instrumental variable decomposition","volume":"16","author":"Yuan Junkun","year":"2022","unstructured":"Junkun Yuan, Anpeng Wu, Kun Kuang, et\u00a0al. 2022. Auto IV: Counterfactual prediction via automatic instrumental variable decomposition. ACM Transactions on Knowledge Discovery from Data 16, 4 (2022), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10424"},{"key":"e_1_3_2_163_2","first-page":"1437","article-title":"Causal reasoning with ancestral graphs","volume":"9","author":"Zhang Jiji","year":"2008","unstructured":"Jiji Zhang. 2008. Causal reasoning with ancestral graphs. Journal of Machine Learning Research 9, Jul (2008), 1437\u20131474.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_164_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2008.08.001"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0152860"},{"key":"e_1_3_2_166_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature11134"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636423","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3636423","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:35:41Z","timestamp":1750178141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":165,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3636423"],"URL":"https:\/\/doi.org\/10.1145\/3636423","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"2022-08-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}