When a randomized controlled trial (RCT) is infeasible, unethical, or untimely, causal inference from observational data can serve as an effective alternative for scientific and clinical decision-making. However, observational studies harbor methodological vulnerabilities―not only confounding due to lack of randomization, but also selection bias or immortal time, arising from flawed study designs―that can fundamentally distort effect estimates. Target trial emulation has gained prominence as a framework for addressing these challenges. This approach has two steps：(1) specifying the protocol of a hypothetical pragmatic RCT (the target trial) that would answer the causal question of interest, and (2) explicitly emulating that trial with existing observational data. Its greatest contribution is the elimination of ambiguous causal questions in observational studies, transforming them into well-defined causal estimands. In this article, we synthesize the conceptual foundations of target trial emulation and detail methodological considerations for its implementation. As an illustrative example, we describe an observational study that compared denosumab with oral bisphosphonates for cardiovascular safety and fracture-prevention effectiveness in maintenance dialysis patients with osteoporosis. The target trial framework offers a structured approach that prevents design-induced biases and clarifies the limitations inherent in observational data, thereby enabling epidemiologists who grapple with causal questions to draw more valid inferences.