Effect of helmet use on severity of head injuries using doubly robust estimators

© Springer International Publishing AG 2017. Causal inference based on observational data can be formulated as a missing outcome imputation and an adjustment for covariate imbalance models. Doubly robust estimators–a combination of imputation-based and inverse probability weighting estimators–offer...

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Main Authors:	Jirakom Sirisrisakulchai, Songsak Sriboonchitta
Format:	Book Series
Published:	2018
Subjects:	Computer Science
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012918265&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57113
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-57113
record_format	dspace
spelling	th-cmuir.6653943832-571132018-09-05T03:35:09Z Effect of helmet use on severity of head injuries using doubly robust estimators Jirakom Sirisrisakulchai Songsak Sriboonchitta Computer Science © Springer International Publishing AG 2017. Causal inference based on observational data can be formulated as a missing outcome imputation and an adjustment for covariate imbalance models. Doubly robust estimators–a combination of imputation-based and inverse probability weighting estimators–offer some protection against some particular misspecified assumptions. When at least one of the two models is correctly specified, doubly robust estimators are asymptotically unbiased and consistent. We reviewed and applied the doubly robust estimators for estimating causal effect of helmet use on the severity of head injury from observational data. We found that helmet usage has a small effect on the severity of head injury. 2018-09-05T03:35:09Z 2018-09-05T03:35:09Z 2017-02-01 Book Series 1860949X 2-s2.0-85012918265 10.1007/978-3-319-50742-2_29 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012918265&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57113
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Computer Science
spellingShingle	Computer Science Jirakom Sirisrisakulchai Songsak Sriboonchitta Effect of helmet use on severity of head injuries using doubly robust estimators
description	© Springer International Publishing AG 2017. Causal inference based on observational data can be formulated as a missing outcome imputation and an adjustment for covariate imbalance models. Doubly robust estimators–a combination of imputation-based and inverse probability weighting estimators–offer some protection against some particular misspecified assumptions. When at least one of the two models is correctly specified, doubly robust estimators are asymptotically unbiased and consistent. We reviewed and applied the doubly robust estimators for estimating causal effect of helmet use on the severity of head injury from observational data. We found that helmet usage has a small effect on the severity of head injury.
format	Book Series
author	Jirakom Sirisrisakulchai Songsak Sriboonchitta
author_facet	Jirakom Sirisrisakulchai Songsak Sriboonchitta
author_sort	Jirakom Sirisrisakulchai
title	Effect of helmet use on severity of head injuries using doubly robust estimators
title_short	Effect of helmet use on severity of head injuries using doubly robust estimators
title_full	Effect of helmet use on severity of head injuries using doubly robust estimators
title_fullStr	Effect of helmet use on severity of head injuries using doubly robust estimators
title_full_unstemmed	Effect of helmet use on severity of head injuries using doubly robust estimators
title_sort	effect of helmet use on severity of head injuries using doubly robust estimators
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012918265&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57113
_version_	1681424818010849280

Effect of helmet use on severity of head injuries using doubly robust estimators

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