Causal Inference in Clinical Research: A Primer

The study of causal relationships is central to scientific inquiry. Understanding what disease is, why it arises, and how it can be treated are a set of fundamentally causal questions. In this article, an overview of causal inference is provided for the applied researcher, with an emphasis placed on intuition and the use of examples from periodontology to support understanding. Causal reasoning goes beyond the description of associations, with a focus instead placed on questions of the form: what would happen to an outcome if a particular exposure were changed? Directed acyclic graphs are introduced as tools through which assumptions can be made explicit and structures that may threaten valid causal inference can be identified, including confounding, mediation, and collider pathways. Randomized controlled trials are highlighted as the gold standard of causal evidence, primarily because they ensure that, on average, comparisons are made between like-with-like groups. However, observational data must often be relied upon due to ethical, practical, and logistical constraints. A three-step framework to answering causal questions is presented, based on (i) asking a causal question, (ii) identifying the causal effect, and (iii) carrying out the analysis. Within this context, two principal strategies for causal identification are outlined: design-based identification and adjustment-based identification. It is illustrated that each approach relies on different sets of assumptions to connect observed data to causal effects. The analysis stage carries out a pre-defined associational comparison that is taken to have a causal interpretation under the proposed assumptions. Across all approaches, a central theme is that causal inference is inherently driven by assumptions. The credibility of a causal claim is determined not primarily by the statistical method, but by whether the underlying assumptions are considered plausible within the context of the scientific question.