Methodological guide demonstrates directed acyclic graph implementation for causal inference in paediatric observational research, highlighting strategies to avoid analytical bias.
Key Points
To provide pediatric researchers with a practical framework for constructing and utilizing directed acyclic graphs (DAGs) to guide causal inference and statistical adjustment in observational studies.
Clarified appropriate use cases by distinguishing causal questions from purely descriptive or predictive research designs.
Outlined principles for DAG construction and analytical adjustment while addressing structural pitfalls such as collider bias and intermediate mediator adjustment.
Demonstrated practical implementation and solutions for temporal ordering ambiguity using a worked example from published pediatric literature.
Explicit causal modeling with DAGs systematically identifies minimally sufficient adjustment sets to control confounding without introducing bias.
Applying graphical causal models prevents erroneous adjustment for colliders and mediators, substantially strengthening the validity of observational child health research.