{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:44:52Z","timestamp":1773215092072,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,16]]},"DOI":"10.1145\/3774816.3774835","type":"proceedings-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:48:11Z","timestamp":1771843691000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Propensity Score Estimation for Causal Inference in Observational Healthcare Studies"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7238-9934","authenticated-orcid":false,"given":"Nabila Sekar","family":"Ramadhanti","sequence":"first","affiliation":[{"name":"Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9782-1128","authenticated-orcid":false,"given":"Suryani","family":"Lim","sequence":"additional","affiliation":[{"name":"Institute of Innovation, Science and Sustainability, Federation University, Gippsland, Victoria, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3533-2913","authenticated-orcid":false,"given":"Michal","family":"Chorev","sequence":"additional","affiliation":[{"name":"IBM Consulting, IBM, Melbourne, Victoria, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7052-0413","authenticated-orcid":false,"given":"Madhu","family":"Chetty","sequence":"additional","affiliation":[{"name":"Institute of Innovation, Science and Sustainability, Federation University, Gippsland, Victoria, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6164-9941","authenticated-orcid":false,"given":"Fadi","family":"Charchar","sequence":"additional","affiliation":[{"name":"Health Innovation and Transformation Centre, Federation University, Ballarat, Victoria, Australia"}]}],"member":"320","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"S. Alam E.\u00a0E.\u00a0M. Moodie and D.\u00a0A. Stephens. 2019. Should a propensity score model be super? The utility of ensemble procedures for causal adjustment. Stat Med 38 9 (2019) 1690\u20131702. 10.1002\/sim.8075","DOI":"10.1002\/sim.8075"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"P.\u00a0C. Austin. 2011. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res 46 3 (2011) 399\u2013424. 10.1080\/00273171.2011.568786","DOI":"10.1080\/00273171.2011.568786"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","unstructured":"Yuan Bian Yu Shi Hui Guo Grace\u00a0Y. Yi and Wenqing He. 2024. Physician Effects in Critical Care: A Causal Inference Approach Through Propensity Weighting with Parametric and Super Learning Methods. Journal of Data Science (2024) 1\u201319. 10.6339\/24-jds1143","DOI":"10.6339\/24-jds1143"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","unstructured":"J.\u00a0W. Chen D.\u00a0R. Maldonado B.\u00a0L. Kowalski K.\u00a0B. Miecznikowski C. Kyin J.\u00a0A. Gornbein and B.\u00a0G. Domb. 2022. Best Practice Guidelines for Propensity Score Methods in Medical Research: Consideration on Theory Implementation and Reporting. A Review. Arthroscopy 38 2 (2022) 632\u2013642. 10.1016\/j.arthro.2021.06.037","DOI":"10.1016\/j.arthro.2021.06.037"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"Vincent Dorie Jennifer Hill Uri Shalit Marc Scott and Dan Cervone. 2019. Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition. Statist. Sci. 34 1 (2019). 10.1214\/18-sts667","DOI":"10.1214\/18-sts667"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"A. Ebrahim\u00a0Valojerdi and L. Janani. 2018. A brief guide to propensity score analysis. Med J Islam Repub Iran 32 (2018) 122. 10.14196\/mjiri.32.122","DOI":"10.14196\/mjiri.32.122"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","unstructured":"Safoora Gharibzadeh Mohammad\u00a0Ali Mansournia Abbas Rahimiforoushani Ahad Alizadeh Atieh Amouzegar Kamran Mehrabani-Zeinabad and Kazem Mohammad. 2018. Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores. Communications in Statistics - Simulation and Computation 47 4 (2018) 964\u2013976. 10.1080\/03610918.2017.1300267","DOI":"10.1080\/03610918.2017.1300267"},{"key":"e_1_3_3_1_9_2","unstructured":"Noah Greifer and Elizabeth\u00a0A. Stuart. 2023. Choosing the Causal Estimand for Propensity Score Analysis of Observational Studies. arxiv:https:\/\/arXiv.org\/abs\/2106.10577\u00a0[stat.ME] https:\/\/arxiv.org\/abs\/2106.10577"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"B.\u00a0K. Lee J. Lessler and E.\u00a0A. Stuart. 2010. Improving propensity score weighting using machine learning. Stat Med 29 3 (2010) 337\u201346. 10.1002\/sim.3782","DOI":"10.1002\/sim.3782"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","unstructured":"D.\u00a0F. McCaffrey G. Ridgeway and A.\u00a0R. Morral. 2004. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods 9 4 (2004) 403\u201325. 10.1037\/1082-989X.9.4.403","DOI":"10.1037\/1082-989X.9.4.403"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","unstructured":"R. Pirracchio and M. Carone. 2018. The Balance Super Learner: A robust adaptation of the Super Learner to improve estimation of the average treatment effect in the treated based on propensity score matching. Stat Methods Med Res 27 8 (2018) 2504\u20132518. 10.1177\/0962280216682055","DOI":"10.1177\/0962280216682055"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Eric\u00a0C Polley and J Mark. 2010. Super Learner In Prediction. (2010).","DOI":"10.32614\/CRAN.package.SuperLearner"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3707127.3707137"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Paul\u00a0R. Rosenbaum and Donald\u00a0B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70 1 (1983) 41\u201355. 10.1093\/biomet\/70.1.41","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Donald Rubin. 1972. Estimating causal effects of treatments in experimental and observational studies. ETS Research Bulletin Series 1972 2 (Dec. 1972) i\u201331.","DOI":"10.1002\/j.2333-8504.1972.tb00631.x"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","unstructured":"M. Salditt and S. Nestler. 2023. Parametric and nonparametric propensity score estimation in multilevel observational studies. Stat Med 42 23 (2023) 4147\u20134176. 10.1002\/sim.9852","DOI":"10.1002\/sim.9852"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","unstructured":"S. Setoguchi S. Schneeweiss M.\u00a0A. Brookhart R.\u00a0J. Glynn and E.\u00a0F. Cook. 2008. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiol Drug Saf 17 6 (2008) 546\u201355. 10.1002\/pds.1555","DOI":"10.1002\/pds.1555"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"M.\u00a0J. van\u00a0der Laan E.\u00a0C. Polley and A.\u00a0E. Hubbard. 2007. Super learner. Stat Appl Genet Mol Biol 6 (2007) Article25. 10.2202\/1544-6115.1309","DOI":"10.2202\/1544-6115.1309"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","unstructured":"D. Westreich J. Lessler and M.\u00a0J. Funk. 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 (2010) 826\u201333. 10.1016\/j.jclinepi.2009.11.020","DOI":"10.1016\/j.jclinepi.2009.11.020"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"J.\u00a0D. Wilkinson M.\u00a0A. Mamas and E. Kontopantelis. 2022. Logistic regression frequently outperformed propensity score methods especially for large datasets: a simulation study. J Clin Epidemiol 152 (2022) 176\u2013184. 10.1016\/j.jclinepi.2022.09.009","DOI":"10.1016\/j.jclinepi.2022.09.009"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"R. Wyss A.\u00a0R. Ellis M.\u00a0A. Brookhart C.\u00a0J. Girman M. Jonsson\u00a0Funk R. LoCasale and T. Sturmer. 2014. The role of prediction modeling in propensity score estimation: an evaluation of logistic regression bCART and the covariate-balancing propensity score. Am J Epidemiol 180 6 (2014) 645\u201355. 10.1093\/aje\/kwu181","DOI":"10.1093\/aje\/kwu181"}],"event":{"name":"HIKM '25: Health Informatics Knowledge Management Conference 2025","location":"Online Australia","acronym":"HIKM 2025"},"container-title":["Proceedings of the 2025 18th Health Informatics Knowledge Management Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774816.3774835","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T09:50:34Z","timestamp":1773136234000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774816.3774835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":21,"alternative-id":["10.1145\/3774816.3774835","10.1145\/3774816"],"URL":"https:\/\/doi.org\/10.1145\/3774816.3774835","relation":{},"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"2026-02-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}