Target Trial Emulation in Health Policy Evaluation: Translation and Challenges from Individual Interventions to Population Effects

Randomized controlled trials (RCTs) represent the "gold standard" for estimating the causal effects of interventions; however, their implementation in the field of health policy evaluation is frequently hindered by logistical feasibility and ethical constraints. Target Trial Emulation (TTE), a framework originating in clinical epidemiology, facilitates rigorous causal inference from observational data by explicitly emulating the design of an idealized "target trial." Recently, the application of TTE has transitioned from individual-level clinical interventions-such as pharmacological or surgical treatments-to population-level health policy evaluations. This interdisciplinary translation is not a localized conceptual shift but necessitates a series of comprehensive methodological adaptations. This paper systematically delineates the core logic of extending the TTE framework into the realm of health policy, providing a profound analysis of the transformation and reconstruction of critical elements, including study units, intervention definitions, time zero, causal estimands, and analytical strategies. Furthermore, it examines the unique challenges inherent in policy contexts, such as policy heterogeneity, staggered adoption, concurrent policy interference, and data granularity limitations. The paper also evaluates the integration of analytical methods, such as instrumental variables (IV) and difference-in-differences (DID), within the TTE framework. This synthesis aims to provide methodological guidance and prospective insights for conducting high-quality policy evaluations using real-world data (RWD).