Distinguishing Description, Prediction, and Causal Inference: A Primer on Improving Congruence Between Research Questions and Methods

This primer introduces the domains into which the aims of quantitative health research generally fall and provides tools to improve the methodological quality of observational clinical and population-based research articles—with a special focus on the field of neurology. Generally, research questions can be categorized into one of the following 3 data science domains: description, prediction, and causal inference. A descriptive question aims to quantify and describe the frequency and distribution of a given health condition in a certain population at or during a specific time. A predictive question aims to estimate either the probability of the presence of a given disease or health condition in an individual (diagnostic prediction) or the probability of an individual developing a disease of interest over a specified period (prognostic prediction). A causal question aims to estimate the causal effect of interest (estimand) of an exposure or intervention on an outcome in a given population. Depending on the research question, estimands could be the total causal effect, a mediated indirect effect, or effect (measure) modification by third variables, among others. Each of these domains comes with its own set of research methods, study designs, reporting guidelines, scientific language, strengths, and limitations, whereby the correct attribution of a research domain will have an impact in 3 ways: i) help authors to formulate appropriate research questions and choose and implement suitable study designs and methods; ii) allow reviewers and editors to assess studies with an increased focus on their clinical relevance, methodological advances, and novelty and quality of clinical evidence; and iii) facilitate clear communication of findings and clinical implications to the broader research community in neurology and related fields.