On the definition of a confounder
- PMID: 25544784
- PMCID: PMC4276366
- DOI: 10.1214/12-aos1058
On the definition of a confounder
Abstract
The causal inference literature has provided a clear formal definition of confounding expressed in terms of counterfactual independence. The causal inference literature has not, however, produced a clear formal definition of a confounder, as it has given priority to the concept of confounding over that of a confounder. We consider a number of candidate definitions arising from various more informal statements made in the literature. We consider the properties satisfied by each candidate definition, principally focusing on (i) whether under the candidate definition control for all "confounders" suffices to control for "confounding" and (ii) whether each confounder in some context helps eliminate or reduce confounding bias. Several of the candidate definitions do not have these two properties. Only one candidate definition of those considered satisfies both properties. We propose that a "confounder" be defined as a pre-exposure covariate C for which there exists a set of other covariates X such that effect of the exposure on the outcome is unconfounded conditional on (X, C) but such that for no proper subset of (X, C) is the effect of the exposure on the outcome unconfounded given the subset. A variable that helps reduce bias but not eliminate bias we propose referring to as a "surrogate confounder."
Keywords: Adjustment; causal diagrams; causal inference; confounder; counterfactuals; minimal sufficiency.
Figures
References
-
- Barnow BS, Cain GG, Goldberger AS. Issues in the analysis of selectivity bias. In: Stromsdorfer E, Farkas G, editors. Evaluation Studies. Vol. 5. Sage; San Francisco: 1980.
-
- Breslow NE, Day NE. Satistical Methods in Cancer Research, vol. 1: The Analysis of Case-Control Studies. International Agency for Research on Cancer; Lyon: 1980. - PubMed
-
- Cox DR. Planning of Experiments. John Wiley & Sons; New York: 1958.
-
- Dawid AP. Influence diagrams for causal modelling and inference. Int. Statist. Rev. 2002;70:161–189.
-
- Geng Z, Guo JH, Fung WK. Criteria for confounders in epidemiological studies. Journal of the Royal Statistical Society, Series B. 2002;64:3–15.
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources