In observational studies, multivariable analysis is commonly used to control confounding and reduce bias in the estimation of causal effect between exposure and outcome. However, in clinical problems with complex causal relationships, researchers select covariates for adjustment through clinical intuition and data-driven methods, which may lead to biased results. In recent years, directed acyclic graphs (DAGs) have become a popular method for visualizing causal relationships between variables. An appropriately constructed DAG can help researchers identify confounders, intermediate variables and other non-confounding variables, thereby improving covariates selection for multivariable analysis. In practice, researchers should incorporate clinical knowledge, systematic methods and transparent reporting to fully utilize DAG in causal inference, and support more reliable clinical decisions.
Randomized controlled trial (RCT) are considered the "gold standard" for evaluating the causal effects of interventions on outcome measures. However, due to high research costs and ethical constraints, conducting RCT in clinical practice, especially in the surgical field, faces numerous challenges such as difficulties in subject recruitment, implementation of blinding, and standardization of interventions. In such cases, using real-world data to perform causal inference under the framework of target trial emulation (TTE), based on the principles of RCT design, helps to identify and reduce biases arising from design flaws in traditional observational studies, such as immortal time bias, confounding, selection bias, or collider bias. This approach can produce high-quality evidence comparable to that of RCT, thereby enhancing the clinical guidance value of real-world data studies. However, TTE has limitations, such as the inability to completely eliminate confounding, high quality requirements for source data, and the current lack of reporting standards. Therefore, researchers should be fully aware of these limitations to avoid making incorrect causal inferences. This article intends to provide an overview of the TTE framework, implementation points, application scope, application cases, and advantages and disadvantages of the framework.