乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T11:15:49Z","timestamp":1724152549716},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s10618-021-00797-x","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T04:02:47Z","timestamp":1632283367000},"page":"2467-2497","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Continuous treatment effect estimation via generative adversarial de-confounding"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5524-5185","authenticated-orcid":false,"given":"Kun","family":"Kuang","sequence":"first","affiliation":[]},{"given":"Yunzhe","family":"Li","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Hongxia","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jianrong","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"797_CR1","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the 34th international conference on machine learning, PMLR, proceedings of machine learning research, vol\u00a070, pp 214\u2013223"},{"key":"797_CR2","doi-asserted-by":"publisher","first-page":"7353","DOI":"10.1073\/pnas.1510489113","volume":"113","author":"S Athey","year":"2016","unstructured":"Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci 113:7353\u20137360","journal-title":"Proc Natl Acad Sci"},{"issue":"4","key":"797_CR3","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1111\/rssb.12268","volume":"80","author":"S Athey","year":"2018","unstructured":"Athey S, Imbens GW, Wager S (2018) Approximate residual balancing: debiased inference of average treatment effects in high dimensions. J R Stat Soc: Ser B (Stat Methodol) 80(4):597\u2013623","journal-title":"J R Stat Soc: Ser B (Stat Methodol)"},{"issue":"3","key":"797_CR4","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1080\/00273171.2011.568786","volume":"46","author":"PC Austin","year":"2011","unstructured":"Austin PC (2011) An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res 46(3):399\u2013424","journal-title":"Multivar Behav Res"},{"issue":"4","key":"797_CR5","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1111\/j.1541-0420.2005.00377.x","volume":"61","author":"H Bang","year":"2005","unstructured":"Bang H, Robins JM (2005) Doubly robust estimation in missing data and causal inference models. Biometrics 61(4):962\u2013973","journal-title":"Biometrics"},{"key":"797_CR6","doi-asserted-by":"crossref","unstructured":"Chan D, Ge R, Gershony O, Hesterberg T, Lambert D (2010) Evaluating online ad campaigns in a pipeline: causal models at scale. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 7\u201316","DOI":"10.1145\/1835804.1835809"},{"issue":"3","key":"797_CR7","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1111\/rssb.12129","volume":"78","author":"KG Chan","year":"2016","unstructured":"Chan KG, Yam SC, Zhang Z (2016) Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting. J R Stat Soc Ser B Stat Methodol 78(3):673\u2013700","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"797_CR8","doi-asserted-by":"crossref","unstructured":"Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C et\u00a0al (2016) Double machine learning for treatment and causal parameters. arXiv preprint arXiv:1608.00060","DOI":"10.1920\/wp.cem.2016.4916"},{"key":"797_CR9","unstructured":"Duchi J, Namkoong H (2018) Learning models with uniform performance via distributionally robust optimization. arXiv preprint arXiv:1810.08750"},{"key":"797_CR10","volume-title":"Inverse probability tilting for moment condition models with missing data","author":"D Egel","year":"2008","unstructured":"Egel D, Graham BS, de Xavier Pinto CC (2008) Inverse probability tilting for moment condition models with missing data. Single equation models eJournal, Econometrics"},{"key":"797_CR11","unstructured":"Fan J, Imai K, Liu H, Ning Y, Yang X (2016) Improving covariate balancing propensity score: a doubly robust and efficient approach. Technical report"},{"key":"797_CR12","doi-asserted-by":"crossref","unstructured":"Flores CA, Flores-Lagunes A (2009) Identification and estimation of causal mechanisms and net effects of a treatment under unconfoundedness. IZA Institute of Labor Economics Discussion Paper Series","DOI":"10.2139\/ssrn.1423353"},{"issue":"1","key":"797_CR13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1214\/17-AOAS1101","volume":"12","author":"C Fong","year":"2018","unstructured":"Fong C, Hazlett C, Imai K et al (2018) Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements. Ann Appl Stat 12(1):156\u2013177","journal-title":"Ann Appl Stat"},{"key":"797_CR14","unstructured":"Galagate D (2016) Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response functions with applications. Ph.D. thesis"},{"issue":"512","key":"797_CR15","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.1080\/01621459.2014.978005","volume":"110","author":"AF Galvao","year":"2015","unstructured":"Galvao AF, Wang L (2015) Uniformly semiparametric efficient estimation of treatment effects with a continuous treatment. J Am Stat Assoc 110(512):1528\u20131542","journal-title":"J Am Stat Assoc"},{"key":"797_CR16","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672\u20132680"},{"issue":"1","key":"797_CR17","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1093\/pan\/mpr025","volume":"20","author":"J Hainmueller","year":"2012","unstructured":"Hainmueller J (2012) Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit Anal 20(1):25\u201346","journal-title":"Polit Anal"},{"issue":"1","key":"797_CR18","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1198\/jcgs.2010.08162","volume":"20","author":"JL Hill","year":"2011","unstructured":"Hill JL (2011) Bayesian nonparametric modeling for causal inference. J Comput Graph Stat 20(1):217\u2013240","journal-title":"J Comput Graph Stat"},{"key":"797_CR19","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/0470090456.ch7","volume":"226164","author":"K Hirano","year":"2004","unstructured":"Hirano K, Imbens GW (2004) The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data perspectives 226164:73\u201384","journal-title":"Applied Bayesian modeling and causal inference from incomplete-data perspectives"},{"issue":"396","key":"797_CR20","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1080\/01621459.1986.10478354","volume":"81","author":"PW Holland","year":"1986","unstructured":"Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):945\u2013960","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"797_CR21","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1111\/rssb.12027","volume":"76","author":"K Imai","year":"2014","unstructured":"Imai K, Ratkovic M (2014) Covariate balancing propensity score. J R Stat Soc: Ser B (Stat Methodol) 76(1):243\u2013263","journal-title":"J R Stat Soc: Ser B (Stat Methodol)"},{"issue":"467","key":"797_CR22","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1198\/016214504000001187","volume":"99","author":"K Imai","year":"2004","unstructured":"Imai K, Van Dyk DA (2004) Causal inference with general treatment regimes: generalizing the propensity score. J Am Stat Assoc 99(467):854\u2013866","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"797_CR23","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1162\/003465304323023651","volume":"86","author":"GW Imbens","year":"2004","unstructured":"Imbens GW (2004) Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat 86(1):4\u201329","journal-title":"Rev Econ Stat"},{"key":"797_CR24","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751","volume-title":"Causal inference in statistics, social, and biomedical sciences","author":"GW Imbens","year":"2015","unstructured":"Imbens GW, Rubin DB (2015) Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, Cambridge"},{"key":"797_CR25","unstructured":"Kallus N (2019) Generalized optimal matching methods for causal inference. J Mach Learn Res (forthcoming)"},{"key":"797_CR26","unstructured":"Kallus N, Santacatterina M (2019) Kernel optimal orthogonality weighting: a balancing approach to estimating effects of continuous treatments. arXiv, Methodology"},{"key":"797_CR27","unstructured":"Kallus N, Zhou A (2018) Policy evaluation and optimization with continuous treatments. In: International conference on artificial intelligence and statistics, pp 1243\u20131251"},{"issue":"4","key":"797_CR28","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1111\/rssb.12212","volume":"79","author":"EH Kennedy","year":"2017","unstructured":"Kennedy EH, Ma Z, McHugh MD, Small DS (2017) Non-parametric methods for doubly robust estimation of continuous treatment effects. J R Stat Soc: Ser B (Stat Methodol) 79(4):1229\u20131245","journal-title":"J R Stat Soc: Ser B (Stat Methodol)"},{"issue":"2","key":"797_CR29","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/1964897.1964905","volume":"12","author":"R Kohavi","year":"2011","unstructured":"Kohavi R, Longbotham R (2011) Unexpected results in online controlled experiments. ACM SIGKDD Explor Newsl 12(2):31\u201335","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"9","key":"797_CR30","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1002\/hec.3189","volume":"24","author":"N Kreif","year":"2015","unstructured":"Kreif N, Grieve R, D\u00edaz I, Harrison D (2015) Evaluation of the effect of a continuous treatment: a machine learning approach with an application to treatment for traumatic brain injury. Health Econ 24(9):1213\u20131228","journal-title":"Health Econ"},{"key":"797_CR31","doi-asserted-by":"crossref","unstructured":"Kuang K, Cui P, Li B, Jiang M, Yang S (2017) Estimating treatment effect in the wild via differentiated confounder balancing. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 265\u2013274. ACM","DOI":"10.1145\/3097983.3098032"},{"key":"797_CR32","doi-asserted-by":"crossref","unstructured":"Kuang K, Cui P, Athey S, Xiong R, Li B (2018) Stable prediction across unknown environments. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1617\u20131626","DOI":"10.1145\/3219819.3220082"},{"issue":"1","key":"797_CR33","first-page":"1","volume":"14","author":"K Kuang","year":"2019","unstructured":"Kuang K, Cui P, Li B, Jiang M, Wang Y, Wu F, Yang S (2019) Treatment effect estimation via differentiated confounder balancing and regression. ACM Trans Knowl Discov Data (TKDD) 14(1):1\u201325","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"797_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3006898","author":"K Kuang","year":"2020","unstructured":"Kuang K, Cui P, Zou H, Li B, Tao J, Wu F, Yang S (2020) Data-driven variable decomposition for treatment effect estimation. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2020.3006898","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"797_CR35","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.eng.2019.08.016","volume":"6","author":"K Kuang","year":"2020","unstructured":"Kuang K, Li L, Geng Z, Xu L, Zhang K, Liao B, Huang H, Ding P, Miao W, Jiang Z (2020b) Causal inference. Engineering 6(3):253\u2013263","journal-title":"Engineering"},{"issue":"10","key":"797_CR36","doi-asserted-by":"publisher","first-page":"4156","DOI":"10.1073\/pnas.1804597116","volume":"116","author":"SR K\u00fcnzel","year":"2019","unstructured":"K\u00fcnzel SR, Sekhon JS, Bickel PJ, Yu B (2019) Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci 116(10):4156\u20134165","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"797_CR37","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.eng.2019.11.013","volume":"6","author":"F Li","year":"2020","unstructured":"Li F, Li L, Yin J, Zhang Y, Zhou Q, Kuang K (2020a) How to interpret machine knowledge. Engineering 6(3):218\u2013220","journal-title":"Engineering"},{"key":"797_CR38","doi-asserted-by":"crossref","unstructured":"Li M, Kuang K, Zhu Q, Chen X, Guo Q, Wu F (2020b) IB-M: a flexible framework to align an interpretable model and a black-box model. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 643\u2013649. IEEE","DOI":"10.1109\/BIBM49941.2020.9313119"},{"issue":"3","key":"797_CR39","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1111\/biom.12859","volume":"74","author":"J Liu","year":"2018","unstructured":"Liu J, Ma Y, Wang L (2018) An alternative robust estimator of average treatment effect in causal inference. Biometrics 74(3):910\u2013923","journal-title":"Biometrics"},{"key":"797_CR40","unstructured":"Liu Y, Dieng A, Roy S, Rudin C, Volfovsky A (2019) Interpretable almost matching exactly for causal inference. AISTATS"},{"key":"797_CR41","unstructured":"Louizos C, Shalit U, Mooij J, Sontag D, Zemel R, Welling M (2017) Causal effect inference with deep latent-variable models. In: Proceedings of the 31st annual conference on neural information processing systems"},{"issue":"3","key":"797_CR42","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.eng.2019.12.013","volume":"6","author":"C Lu","year":"2020","unstructured":"Lu C, Wang S (2020) The general-purpose intelligent agent. Engineering 6(3):221\u2013226","journal-title":"Engineering"},{"issue":"4","key":"797_CR43","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1037\/1082-989X.9.4.403","volume":"9","author":"DF McCaffrey","year":"2004","unstructured":"McCaffrey DF, Ridgeway G, Morral AR (2004) Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods 9(4):403","journal-title":"Psychol Methods"},{"issue":"2","key":"797_CR44","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.jspi.2005.12.008","volume":"137","author":"R Neugebauer","year":"2007","unstructured":"Neugebauer R, van der Laan M (2007) Nonparametric causal effects based on marginal structural models. J Stat Plan Inference 137(2):419\u2013434","journal-title":"J Stat Plan Inference"},{"key":"797_CR45","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10618-019-00670-y","volume":"34","author":"D Olaya","year":"2020","unstructured":"Olaya D, Coussement K, Verbeke W (2020) A survey and benchmarking study of multitreatment uplift modeling. Data Min Knowl Disc 34:273\u2013308","journal-title":"Data Min Knowl Disc"},{"key":"797_CR46","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"J Pearl","year":"2009","unstructured":"Pearl J (2009) Causality. Cambridge University Press, Cambridge"},{"issue":"3","key":"797_CR47","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.eng.2019.12.012","volume":"6","author":"K Ren","year":"2020","unstructured":"Ren K, Zheng T, Qin Z, Liu X (2020) Adversarial attacks and defenses in deep learning. Engineering 6(3):346\u2013360","journal-title":"Engineering"},{"key":"797_CR48","first-page":"920","volume":"11","author":"J Robins","year":"2001","unstructured":"Robins J, Rotnitzky A (2001) Comment on inference for semiparametric models: some questions and an answer, by P.J. Bickel and J. Kwon. Stat Sin 11:920\u2013936","journal-title":"Bickel and J. Kwon. Stat Sin"},{"key":"797_CR49","doi-asserted-by":"crossref","unstructured":"Robins JM, Hernan MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology","DOI":"10.1097\/00001648-200009000-00011"},{"issue":"1","key":"797_CR50","first-page":"1309","volume":"19","author":"M Rojas-Carulla","year":"2018","unstructured":"Rojas-Carulla M, Sch\u00f6lkopf B, Turner R, Peters J (2018) Invariant models for causal transfer learning. J Mach Learn Res 19(1):1309\u20131342","journal-title":"J Mach Learn Res"},{"issue":"3","key":"797_CR51","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.eng.2019.08.015","volume":"6","author":"G Rong","year":"2020","unstructured":"Rong G, Mendez A, Assi EB, Zhao B, Sawan M (2020) Artificial intelligence in healthcare: review and prediction case studies. Engineering 6(3):291\u2013301","journal-title":"Engineering"},{"issue":"1","key":"797_CR52","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","volume":"70","author":"PR Rosenbaum","year":"1983","unstructured":"Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41\u201355","journal-title":"Biometrika"},{"issue":"5","key":"797_CR53","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1037\/h0037350","volume":"66","author":"DB Rubin","year":"1974","unstructured":"Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66(5):688","journal-title":"J Educ Psychol"},{"key":"797_CR54","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s10618-018-0576-8","volume":"32","author":"K Rudas","year":"2018","unstructured":"Rudas K, Jaroszewicz S (2018) Linear regression for uplift modeling. Data Min Knowl Disc 32:1275\u20131305","journal-title":"Data Min Knowl Disc"},{"issue":"5","key":"797_CR55","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1(5):206\u2013215","journal-title":"Nat Mach Intell"},{"issue":"5","key":"797_CR56","first-page":"612","volume":"109","author":"B Sch\u00f6lkopf","year":"2021","unstructured":"Sch\u00f6lkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y (2021) Toward causal representation learning. Proc IEEE 109(5):612\u2013634","journal-title":"Toward causal representation learning. Proc IEEE"},{"key":"797_CR57","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s10618-014-0383-9","volume":"29","author":"M Soltys","year":"2014","unstructured":"Soltys M, Jaroszewicz S, Rzepakowski P (2014) Ensemble methods for uplift modeling. Data Min Knowl Disc 29:1531\u20131559","journal-title":"Data Min Knowl Disc"},{"key":"797_CR58","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1093\/biomet\/asq035","volume":"97","author":"Z Tan","year":"2010","unstructured":"Tan Z (2010) Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika 97:661\u2013682","journal-title":"Biometrika"},{"key":"797_CR59","doi-asserted-by":"crossref","unstructured":"Tian Q, Kuang K, Jiang K, Wu F, Wang Y (2021) Analysis and applications of class-wise robustness in adversarial training. arXiv preprint arXiv:2105.14240","DOI":"10.1145\/3447548.3467403"},{"key":"797_CR60","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1080\/01621459.2017.1319839","volume":"113","author":"S Wager","year":"2015","unstructured":"Wager S, Athey S (2015) Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc 113:1228\u20131242","journal-title":"J Am Stat Assoc"},{"issue":"8","key":"797_CR61","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1016\/j.jclinepi.2009.11.020","volume":"63","author":"D Westreich","year":"2010","unstructured":"Westreich D, Lessler J, Funk MJ (2010) Propensity score estimation: neural networks, support vector machines, decision trees (cart), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol 63(8):826\u2013833","journal-title":"J Clin Epidemiol"},{"key":"797_CR62","unstructured":"Zhao Q (2016) Covariate balancing propensity score by tailored loss functions. arXiv, Methodology"},{"key":"797_CR63","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1515\/jci-2014-0022","volume":"3","author":"Y Zhu","year":"2015","unstructured":"Zhu Y, Coffman D, Ghosh D (2015) A boosting algorithm for estimating generalized propensity scores with continuous treatments. J Causal Inference 3:25\u201340","journal-title":"J Causal Inference"},{"key":"797_CR64","unstructured":"Zou WY, Shyam S, Mui M, Wang M, Pedersen J, Ghahramani Z (2020) Learning continuous treatment policy and bipartite embeddings for matching with heterogeneous causal effects. arXiv:2004.09703"},{"key":"797_CR65","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1080\/01621459.2015.1023805","volume":"110","author":"J Zubizarreta","year":"2015","unstructured":"Zubizarreta J (2015) Stable weights that balance covariates for estimation with incomplete outcome data. J Am Stat Assoc 110:910\u2013922","journal-title":"J Am Stat Assoc"},{"key":"797_CR66","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1007\/s10618-017-0506-1","volume":"31","author":"I \u017dliobait\u0117","year":"2017","unstructured":"\u017dliobait\u0117 I (2017) Measuring discrimination in algorithmic decision making. Data Min Knowl Disc 31:1060\u20131089","journal-title":"Data Min Knowl Disc"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-021-00797-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-021-00797-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-021-00797-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T05:14:53Z","timestamp":1699506893000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-021-00797-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,22]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["797"],"URL":"https:\/\/doi.org\/10.1007\/s10618-021-00797-x","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,22]]},"assertion":[{"value":"27 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2021","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}