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That is, for every covariate combination, it should be possible to observe both treated and control subjects the covariate distributions should overlap between treatment arms. If the positivity assumption is violated, population\u2010level causal inference necessarily involves some extrapolation. Ideally, a greater amount of uncertainty about the causal effect estimate should be reflected in such situations. With that goal in mind, we construct a Gaussian process model for estimating treatment effects in the presence of practical violations of positivity. Advantages of our method include minimal distributional assumptions, a cohesive model for estimating treatment effects, and more uncertainty associated with areas in the covariate space where there is less overlap. We assess the performance of our approach with respect to bias and efficiency using simulation studies. The method is then applied to a study of critically ill female patients to examine the effect of undergoing right heart catheterization.<\/jats:p>","DOI":"10.1002\/sim.9600","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T02:10:05Z","timestamp":1667787005000},"page":"33-51","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Addressing positivity violations in causal effect estimation using Gaussian process priors"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5187-2667","authenticated-orcid":false,"given":"Angela Yaqian","family":"Zhu","sequence":"first","affiliation":[{"name":"Janssen Research &amp; Development Johnson &amp; Johnson  Raritan New Jersey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandita","family":"Mitra","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology, School of Public Health Rutgers University  Piscataway New\u00a0Jersey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1136\/jech.2004.029496"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwp436"},{"key":"e_1_2_10_4_1","first-page":"309","volume-title":"Assessing Overlap in Covariate Distributions","author":"Imbens GW","year":"2015"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2019.10.014"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpj004"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280210386207"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asn055"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2011.09219"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1353\/obs.2018.0012"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1214\/13\u2010AOAS630"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7919"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1515\/jci\u20102018\u20100024"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2016.1260466"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwy201"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1214\/18\u2010AOAS1231"},{"key":"e_1_2_10_17_1","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen CE","year":"2006"},{"key":"e_1_2_10_18_1","first-page":"1","article-title":"Regression and classification using Gaussian process priors","volume":"6","author":"Neal RM","year":"1998","journal-title":"Bayesian Statistics"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.2307\/2335942"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2739"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1214\/19\u2010BA1195"},{"key":"e_1_2_10_23_1","unstructured":"DuvenaudD.Automatic Model Construction with Gaussian Processes. 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