ADJUSTMENT WHEN COVARIATES ARE FALLIBLE
Pohl S, Sengewald MA, Steyer R (2016)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2016
Publisher: wiley
Edited Volumes: Statistics and Causality: Methods for Applied Empirical Research
Series: Wiley Series in Probability and Statistics
ISBN: 9781118947074
DOI: 10.1002/9781118947074.ch15
Abstract
This chapter discusses adjustment when covariates are not perfectly reliable. It starts with reviewing a theoretical framework that applies to fallible and latent covariates. This framework allows for deriving conditions under which adjustment has to be based on the latent covariate and conditions under which adjustment has to be based on the fallible covariate. As most methodological research has considered the case in which adjustment has to be based on the latent covariates. The author presents analytic derivations, simulation studies, and empirical analyses on the biasing effect of measurement error in covariates for causal effect estimation. The chapter evaluates different approaches to adjust for latent covariates. It considers the role of further covariates for the biasing effect of measurement error in another covariate. For this, the author presents an empirical analysis and discusses results from simulation studies. He concludes the implications for adjustment in empirical applications when covariates are fallible.
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Germany (DE)How to cite
APA:
Pohl, S., Sengewald, M.-A., & Steyer, R. (2016). ADJUSTMENT WHEN COVARIATES ARE FALLIBLE. In Wolfgang Wiedermann, Alexander von Eye (Eds.), Statistics and Causality: Methods for Applied Empirical Research. wiley.
MLA:
Pohl, Steffi, Marie-Ann Sengewald, and Rolf Steyer. "ADJUSTMENT WHEN COVARIATES ARE FALLIBLE." Statistics and Causality: Methods for Applied Empirical Research. Ed. Wolfgang Wiedermann, Alexander von Eye, wiley, 2016.
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