Are All Biases Missing Data Problems?-Reference-Cited by-同舟云学术

Are All Biases Missing Data Problems?

Published:2015-07-12 Issue:3 Volume:2 Page:162-171
ISSN:2196-2995
Container-title:Current Epidemiology Reports
language:en
Short-container-title:Curr Epidemiol Rep

Author:

Howe Chanelle J.,Cain Lauren E.,Hogan Joseph W.

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s40471-015-0050-8.pdf

Reference84 articles.

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2. Rubin DB. Inference and missing data. Biometrika. 1976;63:581–92.

3. Edwards JK, Cole SR, Westreich D. All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework. Int J Epidemiol. 2015;28. Consistent with the present review paper the authors use a simple example to describe causal inference as a problem of missing potential outcomes particularly focusing on the case of estimating a causal effect in the presence of potential bias due to measurement error.

4. Hernán MA, Robins J. Causal Inference Book. http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Accessed 4/9/15, 2015. The authors provide a cohesive introductory text to concepts and methods for causal inference.

5. Hill J. Bayesian nonparametric modeling for causal inference. J Comput Graph Stat. 2011;20(1):217–40.

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