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Causal directed acyclic graphs (DAGs) are crucial for identifying causal effects. However, the standard back-door criterion fails in outcome-based sampling, necessitating new graphical rules for causal inference in these studies.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Causal directed acyclic graphs (DAGs) are standard tools for variable selection in causal effect identification.
  • Outcome-based sampling, like the test-negative design for vaccine effectiveness, poses unique challenges not handled by traditional DAG criteria.
  • The common back-door criterion is insufficient for identifying population average causal effects in outcome-based sampling.

Purpose of the Study:

  • To explain graphically why the standard back-door criterion is inadequate for outcome-based sampling studies.
  • To introduce alternative graphical rules for causal inference in outcome-based sampling.
  • To demonstrate updates in the Dagitty software reflecting these new principles.

Main Methods:

  • Utilized intuitive graphical explanations of causal directed acyclic graphs (DAGs).
  • Developed and described new graphical rules applicable to outcome-based sampling.
  • Illustrated modifications within the Dagitty software.

Main Results:

  • Demonstrated the limitations of the back-door criterion in outcome-based sampling for causal effect identification.
  • Provided alternative graphical criteria for assessing identifiability of the causal odds ratio in such studies.
  • Showcased the integration of these principles into the Dagitty software.

Conclusions:

  • The standard back-door criterion is inappropriate for population average causal effects in outcome-based sampling.
  • New graphical rules are necessary and have been developed for causal inference in these specific study designs.
  • The Dagitty software has been updated to support these advanced causal inference methods.