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Discov. Data"],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.<\/jats:p>","DOI":"10.1145\/3444944","type":"journal-article","created":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T22:24:14Z","timestamp":1620685454000},"page":"1-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":401,"title":["A Survey on Causal Inference"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3828-796X","authenticated-orcid":false,"given":"Liuyi","family":"Yao","sequence":"first","affiliation":[{"name":"Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixuan","family":"Chu","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaliang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Gao","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,5,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X0400400307"},{"key":"e_1_2_1_2_1","volume-title":"Fred Bellott, Jayne Boyd-Zaharias, Jeremy Finn, John Folger, John Johnston, and Elizabeth Word.","author":"Achilles C. 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In Advances in Neural Information Processing Systems. 265--272."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOAS285"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412037"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asn055"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-B"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2019.10.014"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10473858"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1162\/003465302317331982"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asx009"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-STS667"},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the 28th International Conference on Machine Learning. 1097--1104","author":"Dud\u00edk Miroslav","year":"2011","unstructured":"Miroslav Dud\u00edk , John Langford , and Lihong Li . 2011 . Doubly robust policy evaluation and learning . In Proceedings of the 28th International Conference on Machine Learning. 1097--1104 . Miroslav Dud\u00edk, John Langford, and Lihong Li. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning. 1097--1104."},{"key":"e_1_2_1_44_1","volume-title":"Stewart","author":"Egami Naoki","year":"2018","unstructured":"Naoki Egami , Christian J Fong , Justin Grimmer , Margaret E. Roberts , and Brandon M . Stewart . 2018 . How to make causal inferences using texts. arXiv preprint arXiv:1802.02163 (2018). Naoki Egami, Christian J Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. 2018. How to make causal inferences using texts. arXiv preprint arXiv:1802.02163 (2018)."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1476246"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOAS1101"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2002.00021.x"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1177\/0002716203254879"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176350369"},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/10618600.1993.10474623","article-title":"Comparison of multivariate matching methods: Structures, distances, and algorithms","volume":"2","author":"Gu Xing Sam","year":"1993","unstructured":"Xing Sam Gu and Paul R. Rosenbaum . 1993 . Comparison of multivariate matching methods: Structures, distances, and algorithms . Journal of Computational and Graphical Statistics 2 , 4 (1993), 405 -- 420 . Xing Sam Gu and Paul R. Rosenbaum. 1993. Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics 2, 4 (1993), 405--420.","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397269"},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the 2020 SIAM International Conference on Data Mining, SDM. SIAM, 271--279","author":"Guo Ruocheng","year":"2019","unstructured":"Ruocheng Guo , Jundong Li , and Huan Liu . 2019 . Counterfactual evaluation of treatment assignment functions with networked observational data . In Proceedings of the 2020 SIAM International Conference on Data Mining, SDM. SIAM, 271--279 . Ruocheng Guo, Jundong Li, and Huan Liu. 2019. Counterfactual evaluation of treatment assignment functions with networked observational data. In Proceedings of the 2020 SIAM International Conference on Data Mining, SDM. SIAM, 271--279."},{"key":"e_1_2_1_54_1","volume-title":"Learning individual treatment effects from networked observational data. arXiv preprint arXiv:1906.03485","author":"Guo Ruocheng","year":"2019","unstructured":"Ruocheng Guo , Jundong Li , and Huan Liu . 2019. Learning individual treatment effects from networked observational data. arXiv preprint arXiv:1906.03485 ( 2019 ). Ruocheng Guo, Jundong Li, and Huan Liu. 2019. Learning individual treatment effects from networked observational data. arXiv preprint arXiv:1906.03485 (2019)."},{"key":"e_1_2_1_55_1","volume-title":"Murray","author":"Hahn P. Richard","year":"2019","unstructured":"P. Richard Hahn , Vincent Dorie , and Jared S . Murray . 2019 . Atlantic causal inference conference (acic) data analysis challenge 2017. arXiv preprint arXiv:1905.09515 (2019). P. Richard Hahn, Vincent Dorie, and Jared S. Murray. 2019. Atlantic causal inference conference (acic) data analysis challenge 2017. arXiv preprint arXiv:1905.09515 (2019)."},{"key":"e_1_2_1_56_1","first-page":"965","article-title":"Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects","volume":"15","author":"Hahn P. Richard","year":"2017","unstructured":"P. Richard Hahn , Jared S. Murray , and Carlos Carvalho . 2017 . Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects . Bayesian Analysis 15 , 3 (2020), 965 -- 1056 . P. Richard Hahn, Jared S. Murray, and Carlos Carvalho. 2017. Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects. 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In 34th International Conference on Machine Learning-Volume 70. 1414--1423."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/815"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-937X.00044"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_2_1_62_1","unstructured":"Patrik O. Hoyer Dominik Janzing Joris M. Mooij Jonas Peters and Bernhard Sch\u00f6lkopf. 2009. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems. 689--696. Patrik O. Hoyer Dominik Janzing Joris M. Mooij Jonas Peters and Bernhard Sch\u00f6lkopf. 2009. Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems. 689--696."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000292"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/3.2.179"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpr013"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12027"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1162\/003465304323023651"},{"key":"e_1_2_1_69_1","volume-title":"Rubin","author":"Imbens Guido W.","year":"2015","unstructured":"Guido W. Imbens and Donald B . Rubin . 2015 . Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press . Guido W. Imbens and Donald B. Rubin. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press."},{"key":"e_1_2_1_70_1","volume-title":"International Conference on Machine Learning. 3020--3029","author":"Johansson Fredrik","year":"2016","unstructured":"Fredrik Johansson , Uri Shalit , and David Sontag . 2016 . Learning representations for counterfactual inference . In International Conference on Machine Learning. 3020--3029 . Fredrik Johansson, Uri Shalit, and David Sontag. 2016. Learning representations for counterfactual inference. In International Conference on Machine Learning. 3020--3029."},{"key":"e_1_2_1_71_1","volume-title":"Learning weighted representations for generalization across designs. arXiv preprint arXiv:1802.08598","author":"Johansson Fredrik D.","year":"2018","unstructured":"Fredrik D. Johansson , Nathan Kallus , Uri Shalit , and David Sontag . 2018. Learning weighted representations for generalization across designs. arXiv preprint arXiv:1802.08598 ( 2018 ). Fredrik D. Johansson, Nathan Kallus, Uri Shalit, and David Sontag. 2018. Learning weighted representations for generalization across designs. arXiv preprint arXiv:1802.08598 (2018)."},{"key":"e_1_2_1_72_1","unstructured":"Judea Pearl. 2012. Judea Pearl on Potential Outcomes. Retrieved from http:\/\/causality.cs.ucla.edu\/blog\/index.php\/2012\/12\/03\/judea-pearl-on-potential-outcomes\/. Judea Pearl. 2012. Judea Pearl on Potential Outcomes. Retrieved from http:\/\/causality.cs.ucla.edu\/blog\/index.php\/2012\/12\/03\/judea-pearl-on-potential-outcomes\/."},{"key":"e_1_2_1_73_1","volume-title":"Aahlad Manas Puli, and Uri Shalit","author":"Kallus Nathan","year":"2018","unstructured":"Nathan Kallus , Aahlad Manas Puli, and Uri Shalit . 2018 . Removing hidden confounding by experimental grounding. In Advances in Neural Information Processing Systems . 10888--10897. Nathan Kallus, Aahlad Manas Puli, and Uri Shalit. 2018. Removing hidden confounding by experimental grounding. In Advances in Neural Information Processing Systems. 10888--10897."},{"key":"e_1_2_1_74_1","unstructured":"Nathan Kallus and Masatoshi Uehara. 2019. Intrinsically efficient stable and bounded off-policy evaluation for reinforcement learning. In Advances in Neural Information Processing Systems. 3320--3329. Nathan Kallus and Masatoshi Uehara. 2019. Intrinsically efficient stable and bounded off-policy evaluation for reinforcement learning. In Advances in Neural Information Processing Systems. 3320--3329."},{"key":"e_1_2_1_75_1","unstructured":"Nathan Kallus and Angela Zhou. 2018. Confounding-robust policy improvement. In Advances in Neural Information Processing Systems. 9269--9279. Nathan Kallus and Angela Zhou. 2018. Confounding-robust policy improvement. In Advances in Neural Information Processing Systems. 9269--9279."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.474"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brat.2019.103412"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220082"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098032"},{"key":"e_1_2_1_80_1","volume-title":"31st AAAI Conference on Artificial Intelligence.","author":"Kuang Kun","year":"2017","unstructured":"Kun Kuang , Peng Cui , Bo Li , Meng Jiang , Shiqiang Yang , and Fei Wang . 2017 . Treatment effect estimation with data-driven variable decomposition . In 31st AAAI Conference on Artificial Intelligence. Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang, and Fei Wang. 2017. Treatment effect estimation with data-driven variable decomposition. In 31st AAAI Conference on Artificial Intelligence."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_2_1_82_1","unstructured":"Matt J. Kusner Joshua Loftus Chris Russell and Ricardo Silva. 2017. Counterfactual fairness. In Advances in Neural Information Processing Systems. 4066--4076. Matt J. Kusner Joshua Loftus Chris Russell and Ricardo Silva. 2017. Counterfactual fairness. In Advances in Neural Information Processing Systems. 4066--4076."},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328526.3329558"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-985X.00154"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018174"},{"key":"e_1_2_1_86_1","volume-title":"Estimation of individual treatment effect in latent confounder models via adversarial learning. arXiv preprint arXiv:1811.08943","author":"Lee Changhee","year":"2018","unstructured":"Changhee Lee , Nicholas Mastronarde , and Mihaela van der Schaar . 2018. Estimation of individual treatment effect in latent confounder models via adversarial learning. arXiv preprint arXiv:1811.08943 ( 2018 ). Changhee Lee, Nicholas Mastronarde, and Mihaela van der Schaar. 2018. Estimation of individual treatment effect in latent confounder models via adversarial learning. arXiv preprint arXiv:1811.08943 (2018)."},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2016.1260466"},{"key":"e_1_2_1_88_1","volume-title":"Workshop on On-line Trading of Exploration and Exploitation 2. 19--36","author":"Li Lihong","year":"2012","unstructured":"Lihong Li , Wei Chu , John Langford , Taesup Moon , and Xuanhui Wang . 2012 . An unbiased offline evaluation of contextual bandit algorithms with generalized linear models . In Workshop on On-line Trading of Exploration and Exploitation 2. 19--36 . Lihong Li, Wei Chu, John Langford, Taesup Moon, and Xuanhui Wang. 2012. An unbiased offline evaluation of contextual bandit algorithms with generalized linear models. In Workshop on On-line Trading of Exploration and Exploitation 2. 19--36."},{"key":"e_1_2_1_89_1","unstructured":"Sheng Li and Yun Fu. 2017. Matching on balanced nonlinear representations for treatment effects estimation. In Advances in Neural Information Processing Systems. 929--939. Sheng Li and Yun Fu. 2017. Matching on balanced nonlinear representations for treatment effects estimation. In Advances in Neural Information Processing Systems. 929--939."},{"key":"e_1_2_1_90_1","volume-title":"25th International Joint Conference on Artificial Intelligence. 3768--3774","author":"Li Sheng","year":"2016","unstructured":"Sheng Li , Nikos Vlassis , Jaya Kawale , and Yun Fu . 2016 . Matching via dimensionality reduction for estimation of treatment effects in digital marketing campaigns . In 25th International Joint Conference on Artificial Intelligence. 3768--3774 . Sheng Li, Nikos Vlassis, Jaya Kawale, and Yun Fu. 2016. Matching via dimensionality reduction for estimation of treatment effects in digital marketing campaigns. In 25th International Joint Conference on Artificial Intelligence. 3768--3774."},{"key":"e_1_2_1_91_1","unstructured":"Bryan Lim. 2018. Forecasting treatment responses over time using recurrent marginal structural networks. In Advances in Neural Information Processing Systems. 7483--7493. Bryan Lim. 2018. Forecasting treatment responses over time using recurrent marginal structural networks. In Advances in Neural Information Processing Systems. 7483--7493."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.8"},{"key":"e_1_2_1_93_1","unstructured":"Christos Louizos Uri Shalit Joris M. Mooij David Sontag Richard Zemel and Max Welling. 2017. Causal effect inference with deep latent-variable models. In Advances in Neural Information Processing Systems. 6446--6456. Christos Louizos Uri Shalit Joris M. Mooij David Sontag Richard Zemel and Max Welling. 2017. Causal effect inference with deep latent-variable models. 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Computer Science Department Faculty Publication Series ( 2000 ), 80. Doina Precup. 2000. Eligibility traces for off-policy policy evaluation. Computer Science Department Faculty Publication Series (2000), 80."},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1146"},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269267"},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-016-0032-z"},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1596"},{"key":"e_1_2_1_116_1","unstructured":"Microsoft Research. 2019. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. Retrieved from https:\/\/github.com\/microsoft\/EconML. Version 0.x. Microsoft Research. 2019. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. Retrieved from https:\/\/github.com\/microsoft\/EconML. 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Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of Educational Psychology 66, 5 (1974), 688."},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.2307\/2533160"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2000.10474233"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e31820a92c6"},{"key":"e_1_2_1_131_1","volume-title":"Eliminating bias in recommender systems via pseudo-labeling. arXiv preprint arXiv:1910.01444","author":"Saito Yuta","year":"2019","unstructured":"Yuta Saito . 2019. Eliminating bias in recommender systems via pseudo-labeling. arXiv preprint arXiv:1910.01444 ( 2019 ). Yuta Saito. 2019. Eliminating bias in recommender systems via pseudo-labeling. arXiv preprint arXiv:1910.01444 (2019)."},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.3506"},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10473869"},{"key":"e_1_2_1_134_1","volume-title":"International Conference on Machine Learning. 1670--1679","author":"Schnabel Tobias","year":"2016","unstructured":"Tobias Schnabel , Adith Swaminathan , Ashudeep Singh , Navin Chandak , and Thorsten Joachims . 2016 . Recommendations as treatments: Debiasing learning and evaluation . In International Conference on Machine Learning. 1670--1679 . Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. 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Perfect match: A simple method for learning representations for counterfactual inference with neural networks. arXiv preprint arXiv:1810.00656 (2018)."},{"key":"e_1_2_1_137_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00681"},{"key":"e_1_2_1_138_1","first-page":"359","article-title":"Can we learn individual-level treatment policies from clinical data","volume":"21","author":"Shalit Uri","year":"2019","unstructured":"Uri Shalit . 2019 . Can we learn individual-level treatment policies from clinical data ? Biostatistics 21 , 2 (2019), 359 -- 362 . DOI:https:\/\/doi.org\/10.1093\/biostatistics\/kxz043 10.1093\/biostatistics Uri Shalit. 2019. Can we learn individual-level treatment policies from clinical data? Biostatistics 21, 2 (2019), 359--362. DOI:https:\/\/doi.org\/10.1093\/biostatistics\/kxz043","journal-title":"Biostatistics"},{"key":"e_1_2_1_139_1","volume-title":"34th International Conference on Machine Learning-Volume 70","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit , Fredrik D. Johansson , and David Sontag . 2017 . Estimating individual treatment effect: Generalization bounds and algorithms . In 34th International Conference on Machine Learning-Volume 70 . 3076--3085. Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: Generalization bounds and algorithms. In 34th International Conference on Machine Learning-Volume 70. 3076--3085."},{"key":"e_1_2_1_140_1","doi-asserted-by":"publisher","DOI":"10.1177\/0049124111404820"},{"key":"e_1_2_1_141_1","unstructured":"Amit Sharma and Emre Kiciman. 2019. DoWhy: A Python package for causal inference. Retrieved from https:\/\/github.com\/microsoft\/dowhy. Amit Sharma and Emre Kiciman. 2019. DoWhy: A Python package for causal inference. Retrieved from https:\/\/github.com\/microsoft\/dowhy."},{"key":"e_1_2_1_142_1","unstructured":"Eli Sherman and Ilya Shpitser. 2018. Identification and estimation of causal effects from dependent data. In Advances in Neural Information Processing Systems. 9424--9435. Eli Sherman and Ilya Shpitser. 2018. Identification and estimation of causal effects from dependent data. In Advances in Neural Information Processing Systems. 9424--9435."},{"key":"e_1_2_1_143_1","unstructured":"Y. Shimoni C. Yanover E. Karavani and Y. Goldschmnidt. 2018. Benchmarking framework for performance-evaluation of causal inference analysis. ArXiv preprint arXiv:1802.05046 (2018). Y. Shimoni C. Yanover E. Karavani and Y. Goldschmnidt. 2018. Benchmarking framework for performance-evaluation of causal inference analysis. ArXiv preprint arXiv:1802.05046 (2018)."},{"key":"e_1_2_1_144_1","unstructured":"Ilya Shpitser. 2015. Segregated graphs and marginals of chain graph models. In Advances in Neural Information Processing Systems. 1720--1728. Ilya Shpitser. 2015. Segregated graphs and marginals of chain graph models. In Advances in Neural Information Processing Systems. 1720--1728."},{"key":"e_1_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.1091\/mbc.9.12.3273"},{"key":"e_1_2_1_147_1","volume-title":"Prediction, and Search","author":"Spirtes Peter","unstructured":"Peter Spirtes , Clark N. Glymour , Richard Scheines , and David Heckerman . 2000. Causation , Prediction, and Search . MIT Press . Peter Spirtes, Clark N. Glymour, Richard Scheines, and David Heckerman. 2000. Causation, Prediction, and Search. MIT Press."},{"key":"e_1_2_1_148_1","volume-title":"Applied Informatics","author":"Spirtes Peter","unstructured":"Peter Spirtes and Kun Zhang . 2016. Causal discovery and inference: Concepts and recent methodological advances . In Applied Informatics , Vol. 3 . Springer , 3. Peter Spirtes and Kun Zhang. 2016. Causal discovery and inference: Concepts and recent methodological advances. In Applied Informatics, Vol. 3. Springer, 3."},{"key":"e_1_2_1_149_1","doi-asserted-by":"crossref","unstructured":"Jerzy Splawa-Neyman Dorota M. Dabrowska and T. P. Speed. 1990. On the application of probability theory to agricultural experiments. Essay on principles. Section 9.Statistical Science 5 4 (1990) 465--472. Jerzy Splawa-Neyman Dorota M. Dabrowska and T. P. Speed. 1990. On the application of probability theory to agricultural experiments. Essay on principles. Section 9.Statistical Science 5 4 (1990) 465--472.","DOI":"10.1214\/ss\/1177012031"},{"key":"e_1_2_1_150_1","volume-title":"Counterfactuals and Causal Inference: Methods and Principles for Social Research","author":"Stephen Morgan","unstructured":"Morgan Stephen and Winship Christopher . 2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research . Cambridge University Press, Cambridge , UK. Morgan Stephen and Winship Christopher. 2007. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press, Cambridge, UK."},{"key":"e_1_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-STS313"},{"key":"e_1_2_1_152_1","volume-title":"29th AAAI Conference on Artificial Intelligence. 297--303","author":"Sun Wei","year":"2015","unstructured":"Wei Sun , Pengyuan Wang , Dawei Yin , Jian Yang , and Yi Chang . 2015 . Causal inference via sparse additive models with application to online advertising . In 29th AAAI Conference on Artificial Intelligence. 297--303 . Wei Sun, Pengyuan Wang, Dawei Yin, Jian Yang, and Yi Chang. 2015. Causal inference via sparse additive models with application to online advertising. In 29th AAAI Conference on Artificial Intelligence. 297--303."},{"key":"e_1_2_1_153_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V . Le . 2014 . Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems . 3104--3112. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems. 3104--3112."},{"key":"e_1_2_1_154_1","volume-title":"International Conference on Machine Learning. 814--823","author":"Swaminathan Adith","year":"2015","unstructured":"Adith Swaminathan and Thorsten Joachims . 2015 . Counterfactual risk minimization: Learning from logged bandit feedback . In International Conference on Machine Learning. 814--823 . Adith Swaminathan and Thorsten Joachims. 2015. Counterfactual risk minimization: Learning from logged bandit feedback. In International Conference on Machine Learning. 814--823."},{"key":"e_1_2_1_155_1","unstructured":"Adith Swaminathan Akshay Krishnamurthy Alekh Agarwal Miro Dudik John Langford Damien Jose and Imed Zitouni. 2017. Off-policy evaluation for slate recommendation. In Advances in Neural Information Processing Systems. 3632--3642. Adith Swaminathan Akshay Krishnamurthy Alekh Agarwal Miro Dudik John Langford Damien Jose and Imed Zitouni. 2017. Off-policy evaluation for slate recommendation. In Advances in Neural Information Processing Systems. 3632--3642."},{"key":"e_1_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1017"},{"key":"e_1_2_1_157_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280210386779"},{"key":"e_1_2_1_158_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i06.6590"},{"key":"e_1_2_1_159_1","unstructured":"Victor Veitch Yixin Wang and David Blei. 2019. Using embeddings to correct for unobserved confounding in networks. In Advances in Neural Information Processing Systems. 13769--13779. Victor Veitch Yixin Wang and David Blei. 2019. Using embeddings to correct for unobserved confounding in networks. In Advances in Neural Information Processing Systems. 13769--13779."},{"key":"e_1_2_1_160_1","unstructured":"Thomas Verma and Judea Pearl. 1991. Equivalence and Synthesis of Causal Models. UCLA Computer Science Department. Thomas Verma and Judea Pearl. 1991. Equivalence and Synthesis of Causal Models. UCLA Computer Science Department."},{"key":"e_1_2_1_161_1","volume-title":"Human-level control through deep reinforcement learning. Nature 518, 7540","author":"Volodymyr Mnih","year":"2015","unstructured":"Mnih Volodymyr , Kavukcuoglu Koray , Silver David , A Rusu Andrei , and Veness Joel . 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 ( 2015 ), 529--533. Mnih Volodymyr, Kavukcuoglu Koray, Silver David, A Rusu Andrei, and Veness Joel. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529--533."},{"key":"e_1_2_1_162_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_2_1_163_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685294"},{"key":"e_1_2_1_164_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00197"},{"key":"e_1_2_1_165_1","volume-title":"International Conference on Machine Learning. 6638--6647","author":"Wang Xiaojie","year":"2019","unstructured":"Xiaojie Wang , Rui Zhang , Yu Sun , and Jianzhong Qi . 2019 . Doubly robust joint learning for recommendation on data missing not at random . In International Conference on Machine Learning. 6638--6647 . Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2019. Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Machine Learning. 6638--6647."},{"key":"e_1_2_1_167_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"e_1_2_1_168_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2764"},{"key":"e_1_2_1_169_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1488"},{"key":"e_1_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rie.2016.01.001"},{"key":"e_1_2_1_171_1","unstructured":"Liuyi Yao Sheng Li Yaliang Li Mengdi Huai Jing Gao and Aidong Zhang. 2018. Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems. 2633--2643. Liuyi Yao Sheng Li Yaliang Li Mengdi Huai Jing Gao and Aidong Zhang. 2018. Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems. 2633--2643."},{"key":"e_1_2_1_172_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00186"},{"key":"e_1_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/570"},{"key":"e_1_2_1_174_1","volume-title":"6th International Conference on Learning Representations.","author":"Yoon Jinsung","unstructured":"Jinsung Yoon , James Jordon , and Mihaela van der Schaar. 2018. GANITE: Estimation of individualized treatment effects using generative adversarial nets . In 6th International Conference on Learning Representations. Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2018. GANITE: Estimation of individualized treatment effects using generative adversarial nets. In 6th International Conference on Learning Representations."},{"key":"e_1_2_1_175_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358058"},{"key":"e_1_2_1_176_1","volume-title":"25th Conference on Uncertainty in Artificial Intelligence. 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