An introduction to directed acyclic graphs in trauma research

Area of Science:

• Causal inference
• Psychological trauma research
• Data visualization

Background:

• Directed acyclic graphs (DAGs) are underutilized in psychological trauma research.
• DAGs offer a visual method for representing causal relationships in empirical data.

Purpose of the Study:

• To introduce Directed Acyclic Graphs (DAGs) to trauma researchers.
• To explain the utility of DAGs for clarifying causal assumptions and identifying effects.
• To demonstrate how DAGs prevent bias in causal inference.

Main Methods:

• Explanation of DAGs, including basic definitions and rules.
• Illustration of DAGs using a hypothetical example.
• Discussion of how controlling for variables can introduce bias.

Main Results:

• DAGs clarify causal assumptions and identify potential effects.
• A case study shows controlling for covariates can bias results, using PTSD and Parkinson's disease as an example.
• DAGs prevent bias that may arise from controlling for all covariates.

Conclusions:

• DAGs empower trauma researchers to determine when causal conclusions are valid.
• This introduction facilitates understanding of causal inference methods in trauma research.