Associations in Medical Research Can Be Misleading: A Clinician's Guide to Causal Inference

Abstract

Understanding the difference between correlation and causation is essential in medical research, yet this distinction remains a common source of confusion among clinicians and researchers. While correlation indicates that two variables are related, it does not necessarily mean that changes in one variable directly cause changes in the other—a misunderstanding that can lead to misguided clinical decisions and flawed public health policies. Causal inference provides a powerful statistical framework for estimating true causal relationships, even in the absence of randomized controlled trials, which are often constrained by ethical, financial, and logistical limitations. This paper serves as an introductory guide to the methodologies of causal inference, offering clinicians and medical researchers a clear and practical roadmap for distinguishing correlation from causation. It explores two key frameworks: the potential outcomes model, which relies on counterfactual reasoning, and the structural causal model, which uses directed acyclic graphs to visualize and analyze causal relationships. Practical methods for causal estimation—including regression analysis, instrumental variables, propensity score matching, and inverse probability weighting—are discussed in detail, with a focus on their assumptions, strengths, and limitations. The paper also addresses common challenges such as unmeasured confounding, reverse causality, and model misspecification, offering strategies to mitigate bias and enhance the validity of causal estimates. A structured framework for selecting appropriate causal inference methods is provided to guide researchers in applying these techniques effectively in clinical and surgical research. By equipping clinicians with the tools to make evidence-based decisions, this paper aims to strengthen the scientific foundation of medical research and improve patient outcomes.