The widespread use of diagrams to convey abstract information shows it is generally accepted that diagrams can assist in the understanding of abstract concepts, at least sometimes. Research in cognitive science has suggested that diagrams can make it easier to find the information relevant to a concept, such as the causal paths between variables that might lead to selection bias in a study. Diagrams can also help when considering alternative possibilities by making all the possibilities explicit, such as when a researcher is forming conclusions at the end of a study, based partly on alternative explanations for the results.
Causal diagrams, which in most cases are DAGs, provide an intuitive framework that can help researchers conceive of and understand the biases that might influence a study, and can make communicating more difficult concepts easier than explaining solely with words. This makes DAGs a useful tool to enhance the communicating of concepts relating to bias, whether teaching basic concepts or publishing the results of methodological research. This is especially the case with the structural classification of bias, covered in the previous section, but DAGs have also been used to explain more specific types of bias, such as different types of time-dependent confounding, missing data biases, and possible explanations for apparent paradoxes such as Simpson’s paradox, the birth weight paradox, and the obesity paradox.
It is now well established that an analysis of observational data should take into consideration not only the study design, but also substantial background subject-matter knowledge if the goal is to obtain evidence regarding a causal association. Otherwise, important uncontrolled confounding might not be considered when making inferences, or variables might be included in a model that instead of reducing bias, increases it via collider bias. Also, by constructing a causal DAG that aims to adequately represent background causal knowledge, a researcher or statistician might be prompted to include variables that otherwise would not have been considered.
This means that if a DAG is constructed during the planning stage of a study, potential confounders that otherwise might not have been considered, can instead be either controlled by modifying the design, or else have data collected on that variable so it can be used to adjust the analysis. The DAG can also be used to communicate this understanding to fellow investigators or study staff, or to ask for feedback from subject matter experts.
Once a study’s data has been collected, a DAG can be useful in identifying previously unconsidered sources of bias, such as from missing data, loss to follow-up or time-dependent confounding. And this can help plan the analysis with the most appropriate methodology.
It is also possible to use a DAG to identify a minimally sufficient set of variables that is needed to control for confounding in the analysis. This would exclude variables such as intermediates on the causal pathway between the exposure and the outcome. The program DAGitty was recently criticised, however, because it can calculate such a set automatically. This may potentially mislead a researcher into thinking they could successfully control for confounding by adjusting for the variables DAGitty chose, even though important confounders were not included in the DAG.
Finally, a DAG can help with the interpretation and communication of the results. By making the assumptions on which causal inferences rest more explicit, such as the possibility of confounding from sources that were not controlled, conclusions by researchers might be more likely to be adequately cautious. The DAG can, and should, also be included with any published report, to help communicate the sources of bias identified, how they were controlled in the design and the analysis, and the assumptions and associated uncertainty that remains following the analysis. Unfortunately, it is still not uncommon to find articles that merely mention that a DAG was used to help select the model covariates, without providing the DAG itself.