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IJE Advance Access originally published online on March 3, 2008
International Journal of Epidemiology 2008 37(3):624-626; doi:10.1093/ije/dyn035
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2008; all rights reserved.

Commentary: Why are we biased against bias?

Jay S Kaufman

Department of Epidemiology, University of North Carolina, School of Public Health, Chapel Hill, NC 27599-7435, USA. E-mail: jay_kaufman@unc.edu

Accepted 31 January 2008

The first 10% of the full text of this article appears below.

Greater attention to causal inference has been one of the most important trends in social epidemiology over the last decade. The groundwork was laid 35 years ago by Mervyn Susser's book ‘Causal Thinking in the Health Sciences’,1 but growing interest more recently in causal techniques such as potential outcomes models and directed graphs has given the field new capacities for strengthening inference and honing arguments.2 Many techniques that have been standard in econometrics and the social sciences for years have made their way into social epidemiology in the last decade, including multilevel modeling,3 propensity score matching4 and instrumental variables.5 One such technique, exploited cleverly in the article by Gilman and colleagues,6 is the fixed effects regression model.

Epidemiologists have long been enthusiastic users of the same conditional estimator used for fixed . . . [Full Text of this Article]


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