{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:10:41Z","timestamp":1740100241690,"version":"3.37.3"},"reference-count":46,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council (ARC)","doi-asserted-by":"publisher","award":["DP200101374,LP170100891"],"award-info":[{"award-number":["DP200101374,LP170100891"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533959","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["Stochastic Intervention for Causal Effect Estimation"],"prefix":"10.1109","author":[{"given":"Tri Dung","family":"Duong","sequence":"first","affiliation":[]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"journal-title":"DoWhy A Python package for causal inference","year":"0","key":"ref39"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1709"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1177\/0962280213507034"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyn079"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00442"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2005.00377.x"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/BF00175354"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2351356.2351363"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201600094"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1177\/1077558710361486","article-title":"Applying propensity score methods in medical research: pitfalls and prospects","volume":"67","author":"luo","year":"2010","journal-title":"Medical Care Research and Review"},{"key":"ref10","first-page":"1097","article-title":"Doubly robust policy evaluation and learning","author":"dud\u00edk","year":"2011","journal-title":"Proceedings of the 28th International Conference on International Conference on Machine Learning ICML'11"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0383-9"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1214\/14-STS500"},{"journal-title":"Models reasoning and inference","year":"2000","author":"pearl","key":"ref12"},{"key":"ref13","article-title":"Identifying dynamic sequential plans","author":"tian","year":"2012","journal-title":"ArXiv Preprint"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835809"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964905"},{"key":"ref16","first-page":"3207","article-title":"Counterfactual reasoning and learning systems: The example of computational advertising","volume":"14","author":"bottou","year":"2013","journal-title":"The Journal of Machine Learning Research"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.05.179"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00628"},{"key":"ref19","article-title":"Causality learning: A new perspective for interpretable machine learning","author":"xu","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref28","article-title":"Uplift modeling for clinical trial data","author":"jaskowski","year":"0","journal-title":"ICML Workshop on Clinical Data Analysis"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"ref27","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","author":"friedman","year":"2001","journal-title":"Annals of Statistics"},{"key":"ref3","first-page":"265","article-title":"Bayesian ensemble learning","author":"chipman","year":"2007","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"ref29","article-title":"Package &#x2018;uplift&#x2019;","author":"guelman","year":"2014","journal-title":"CRAN"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"ref8","article-title":"Matchit: nonparametric preprocessing for parametric causal inference","author":"stuart","year":"2011","journal-title":"Journal of Statistical Software"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/003465302317331982"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6607"},{"journal-title":"EconML A Python Package for ML-Based Heterogeneous Treatment Effects Estimation","year":"2019","key":"ref9"},{"journal-title":"tmle An r package for targeted maximum likelihood estimation","year":"2011","author":"gruber","key":"ref1"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.070"},{"key":"ref20","article-title":"Econometric theory","author":"goldberger","year":"1964","journal-title":"Econometric Theory"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_14"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.2174\/1876399500901010016"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2013.23"},{"journal-title":"Npci Non-parametrics for causal inference","year":"2016","author":"dorie","key":"ref42"},{"key":"ref24","article-title":"A proportional hazards approach to campaign list selection","author":"manahan","year":"0","journal-title":"SAS User Group International (SUGI) 30 Proceedings"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1017\/pan.2017.15"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1002\/dir.10035"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3111957"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2004.1380102"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.66"},{"key":"ref25","first-page":"18","article-title":"Classification and regression by randomforest","volume":"2","author":"liaw","year":"2002","journal-title":"R News"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533959.pdf?arnumber=9533959","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:50Z","timestamp":1652197550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533959\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533959","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}