Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55549
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dc.contributor.authorKanchit Suknarken_US
dc.contributor.authorJirakom Sirisrisakulchaien_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T02:57:47Z-
dc.date.available2018-09-05T02:57:47Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-84952682973en_US
dc.identifier.other10.1007/978-3-319-27284-9_19en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952682973&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55549-
dc.description.abstract© Springer International Publishing Switzerland 2016. The researchers reinvestigate the effect of alcohol consumption on hypertension from observational data, taken from the Thai National Health Examination Survey. In the observed samples, the treatment assignment is not ignorable, thus using treatment as a dummy variable in the statistical model will lead to the bias estimation of treatment effects. Factors affecting self-selection (drink/not drink) may cause the dummy variable of treatment to be correlated with random errors in the outcome model, which leads to the biased parameters estimation. We propose to use copula-based endogenous switching regression for ordinal outcomes as the more appropriate model for treatment effect estimation. The new results should give us more a accurate and reliable treatment effect for causal inference.en_US
dc.subjectComputer Scienceen_US
dc.titleReinvestigating the effect of alcohol consumption on hypertension diseaseen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume622en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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