Year: 2011
Type: editorial
Abstract: In choosing covariates for adjustment or inclusion in propensity score analysis, researchers must weigh the benefit of reducing confounding bias carried by those covariates against the risk of amplifying residual bias carried by unmeasured confounders. The latter is characteristic of covariates that act like instrumental variables-that is, variables that are more strongly associated with the exposure than with the outcome. In this issue of the Journal (Am J Epidemiol. 2011;174(11):1213-1222), Myers et al. compare the bias amplification of a near-instrumental variable with its bias-reducing potential and suggest that, in practice, the latter outweighs the former. The author of this commentary sheds broader light on this comparison by considering the cumulative effects of conditioning on multiple covariates and showing that bias amplification may build up at a faster rate than bias reduction. The author further derives a partial order on sets of covariates which reveals preference for conditioning on outcome-related, rather than exposure-related, confounders.
Source: American Journal of Epidemiology
DOI: 10.1093/aje/kwr352
Author: J. Pearl
Institution: University of California, Los Angeles
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: RI: Small: Modeling and Parsing Time Series for Causal Analysis with Application to Action Interpretation in Video of Natural and Man-made Environments, G2237907241, Data Structuring and Visualization System for Neuro-oncology, G4761447359, G5310329857, RI: Small: Probabilistic Networks for Automated Reasoning, G6822419468, G8876996369
OpenAlex ID: W2147646215