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: Sirisrisakulchai J., Sriboonchitta S.
Format: Book Series
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012918265&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40743
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Institution: Chiang Mai University
id th-cmuir.6653943832-40743
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spelling th-cmuir.6653943832-407432017-09-28T04:11:13Z Effect of helmet use on severity of head injuries using doubly robust estimators Sirisrisakulchai J. Sriboonchitta S. © 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. 2017-09-28T04:11:13Z 2017-09-28T04:11:13Z 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/6653943832/40743
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
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 Sirisrisakulchai J.
Sriboonchitta S.
spellingShingle Sirisrisakulchai J.
Sriboonchitta S.
Effect of helmet use on severity of head injuries using doubly robust estimators
author_facet Sirisrisakulchai J.
Sriboonchitta S.
author_sort Sirisrisakulchai J.
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 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012918265&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40743
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