Most clinical questions in neurology are inherently causal, yet observational studies have long been interpreted in terms of associations rather than causal effects of well-defined interventions. This limitation has contributed to persistent biases in neurology research, including inappropriate covariate adjustment, temporal misalignment, immortal-time bias, and susceptible depletion. Recent developments in causal inference offer a formal framework to overcome these issues through counterfactual reasoning, causal diagrams, and g-methods. The target trial emulation approach translates these concepts into a practical strategy: investigators first specify the protocol of the randomized trial that would ideally address their question, and then emulate this protocol using observational data. This requires explicit definition of eligibility, treatment strategies, assignment procedures, time zero, follow-up, outcomes, causal contrasts, and handling of intercurrent events, together with prespecified causal assumptions and an appropriate analysis plan. By aligning the design of observational studies with the structure of randomized trials, target trial emulation reduces design-related bias, clarifies the causal question, and provides a transparent foundation for real-world evidence generation. Validation studies, including recent work in multiple sclerosis, have demonstrated good concordance between emulated trials using observational data and randomized controlled trials. Target trial emulation therefore represents an essential complement to randomized evidence, particularly for questions that are difficult or impossible to address through randomization, and has the potential to substantially improve causal inference in neurology.