Principles of confounder selection

By Tyler J. VanderWeele. European Journal of Epidemiology, 2019

1,282 citations recorded by the source corpus.

Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9aede196323b0482e25bd7aa520d9ce235497ce0

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Citations in the corpus

Reporting guidelines for Human Microbiome Research: the STORMS checklist (2021, methodology): “Methods include, for example, the use of multivariable analysis or matching to adjust for confounding variables between a hypothesized exposure (such as abundance of a microbial taxon) and the disease or condition under study 29 .”
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations (2020, background): “However, since the true data-generating process can never be known, it must be postulated from expert knowledge, relevant theory and plausible assumptions.(10,11) Directed acyclic graphs (DAGs) provide a simple and transparent way for observational data scientists to identify and demonstrate their k”