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Principles of confounder selection.

Tyler J VanderWeele1

  • 1Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. tvanderw@hsph.harvard.edu.

European Journal of Epidemiology
|March 7, 2019
PubMed
Summary
This summary is machine-generated.

This study offers a practical method for selecting confounders in causal inference when full causal diagrams are unknown. It guides researchers on controlling for variables that are causes of exposure or outcome, excluding instrumental variables, and including proxies for unmeasured common causes.

Keywords:
Causal inferenceColliderConfounderCovariate adjustmentSelection

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Reliable causal inference hinges on appropriate confounder selection.
  • Existing methods often require complete knowledge of causal diagrams, which is frequently unavailable.
  • There is a need for practical confounder selection strategies under less stringent assumptions.

Purpose of the Study:

  • To propose a practical approach for confounder selection in causal inference.
  • To provide clear decision rules for covariate control when complete causal knowledge is lacking.
  • To integrate theoretical developments with practical application in confounder selection.

Main Methods:

  • The study proposes a method based on partial knowledge of covariate relationships (cause of exposure/outcome).
  • Decision rules include controlling for causes of exposure or outcome, excluding instrumental variables, and including proxies for unmeasured common causes.
  • Theoretical developments in causal inference literature underpin the proposed methodology.

Main Results:

  • A practical framework for confounder selection is presented, applicable when full causal diagrams are unknown.
  • The proposed rules offer a systematic way to decide on covariate control.
  • The approach aims to enhance the reliability of causal inference in real-world scenarios.

Conclusions:

  • The proposed approach provides a feasible strategy for confounder selection in observational studies.
  • This method facilitates more robust causal inference by guiding appropriate covariate adjustment.
  • The principles discussed can be further related to statistical covariate selection techniques.