Causal Generative AI vs. Target Trial Emulation: Applications in Clinical Decision-Making

The increasing availability of real-world data (RWD) from electronic health records, claims databases, and patient registries has necessitated advanced causal inference methodologies to generate robust clinical insights. Two emerging approaches, Causal Generative AI and Target Trial Emulation (TTE), offer complementary strategies for estimating treatment effects and guiding clinical decision-making. This paper compares these methods, highlighting their strengths, limitations, and optimal applications across different healthcare domains. Causal Generative AI leverages machine learning to generate synthetic patient data and simulate counterfactual treatment outcomes, making it particularly useful when datasets lack direct linkage or contain missing variables. This approach excels in precision medicine applications, where "what-if" scenarios inform personalized treatment strategies. Target Trial Emulation, on the other hand, structures observational data to mimic a randomized controlled trial (RCT), ensuring a more transparent and structured estimation of causal effects when well-defined treatment comparisons exist and patient-level data linkage is feasible. Through case studies in oncology, cardiovascular disease, and perioperative care, we illustrate when each method is best suited. Causal Generative AI is advantageous in fragmented datasets requiring imputation of missing clinical or genomic information, whereas TTE is preferable when emulating an RCT using linked real-world data. The paper advocates for hybrid approaches integrating both methods to enhance causal inference in clinical research. By delineating the appropriate contexts for Causal Generative AI and Target Trial Emulation, this study provides a roadmap for researchers and clinicians to select the most effective methodology for evidence-based decision-making and prospective trial design.