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: | , |
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| Format: | Book Series |
| Published: |
2018
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| Subjects: | |
| 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 |
| Summary: | © 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. |
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