DAGs: Directed Acyclic Graphs for Drawing Assumptions and Guiding Causal Inference.

Area of Science:

• Pediatric Hospital Medicine
• Causal Inference Methodology

Background:

• Observational studies are crucial for hospital-based research but often show correlation, not causation.
• Controlling for confounding and reducing bias are essential to infer causation from observational data.

Purpose of the Study:

• To review Directed Acyclic Graphs (DAGs) as a causal inference tool.
• To guide researchers in building and utilizing DAGs for study design and analysis.

Main Methods:

• Exploration of DAGs as visual tools for causal inference.
• Explanation of variable types (mediators, confounders, colliders) and their causal assumptions.
• Recommendations for integrating DAGs into research design and analytic plans.

Main Results:

• DAGs visually represent assumed relationships among study variables.
• Understanding variable types and their connections is key to DAG construction.
• DAGs inform study design and analysis for causal inference.

Conclusions:

• DAGs are powerful tools for communicating study assumptions and guiding causal inference.
• Effective DAGs depend on the creator's understanding of the research context.
• This methodology equips researchers to build DAGs for more robust observational studies.