Directed acyclic graphs (DAGs) are essential tools based on prior assumptions, capable of clearly depicting causal relationships between variables, aiding in the identification of confounders and assessment of bias. They have been widely applied in the field of epidemiology. However, the construction of DAGs is prone to the subjectivity of researchers. This paper interprets the DAGs construction and application guidelines from the BMJ and elaborates on the seven steps for building DAGs: clarifying the research question and target population, identifying relevant variables, reaching consensus and pre-registering, determining data collection or datasets based on the consensus graph, selecting analysis methods and variable measurement, conducting sensitivity analysis, and reporting the DAGs. By following these steps, researchers can construct DAGs more scientifically, identify the minimal sufficient adjustment set, and enhance the scientific validity and reliability of their studies. Despite its advantages, DAGs have limitations. For instance, failure to consider all relevant factors may lead to unexplained disturbances in the analysis results or incorrect causal inferences, thus failing to verify other key assumptions in causal reasoning. Therefore, it is recommended to integrate DAGs with data-driven methods to maximize their strengths and compensate for their shortcomings.
In observational studies, multivariable analysis is commonly used to control confounding and reduce bias in the estimation of causal effect between exposure and outcome. However, in clinical problems with complex causal relationships, researchers select covariates for adjustment through clinical intuition and data-driven methods, which may lead to biased results. In recent years, directed acyclic graphs (DAGs) have become a popular method for visualizing causal relationships between variables. An appropriately constructed DAG can help researchers identify confounders, intermediate variables and other non-confounding variables, thereby improving covariates selection for multivariable analysis. In practice, researchers should incorporate clinical knowledge, systematic methods and transparent reporting to fully utilize DAG in causal inference, and support more reliable clinical decisions.