Reducing bias through directed acyclic graphs
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- Validation status
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Abstract
BACKGROUND: The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate. DISCUSSION: The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view. SUMMARY: Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.
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The record
- Venue
- BMC Medical Research Methodology
- Topic
- Advanced Causal Inference Techniques
- Field
- Mathematics
- Canadian institutions
- McGill UniversityJewish General Hospital
- Funders
- McGill University Health CentreMcGill University
- Keywords
- Directed acyclic graphConfoundingCausal inferenceSelection biasComputer scienceOutcome (game theory)EconometricsSimple (philosophy)Causality (physics)StatisticsMathematicsAlgorithm
- Has abstract in OpenAlex
- yes