Causal Inference: Onward and Upward!

When announcing the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2021, the Royal Swedish Academy emphasized how conclusions about cause and effect can be drawn from natural experiments. But what can dental research learn from this? The economist's toolbox provides a number of methods for causal inference from observational data such as instrumental variables, regression discontinuity designs, or difference-in-differences analyses. Although the relevance of improving causal inference in dental research has repeatedly been highlighted in recent years, dental research still seems to reveal major room for improvement in the application of such methods. First, there seems to be an absence of causal literature on key essential research questions for oral health. Second, the diversity and diffusion of causal inferential methods in the dental literature seem very limited so far. Third, while dental research has widely been promoting the use of directed acyclic graphs (DAGs) to help conceptualize causal thinking, comparably little attention seems to have been paid to choosing and applying appropriate data-analytic approaches for causal inference. Fourth, similar to other fields of medicine, confusion seems to persist within the dental research community as to the use of causal language. If dental research is to secure a robust evidence base for promoting effective oral health interventions, we argue that dental research needs to move beyond its current methodological echo chamber and embrace a radically different approach to causal inference. We call for editors, reviewers, and authors to embrace a much more critically reflective approach to causal inference.