Causal Inference and Pathway Embeddings with Real-World Data for Enhanced Trial Design Across Diseases

Randomized controlled trials (RCTs) are often considered the gold standard for causal inference, but their implementation can be costly, time-consuming, and sometimes infeasible due to ethical or practical constraints. The so-called target trial emulation framework introduced the systematic use of observational data for treatment effect. This approach necessitates the detailed specification of a hypothetical trial protocol including eligibility criteria, treatment strategies, and outcome measures, which are then emulated by utilizing observational data. We expanded the target trial framework by integrating drug pathway embeddings and causal modeling, enabling prediction of treatment outcomes for unseen or held-out mechanisms of action based on the embedding relationships among existing therapies. We demonstrate that embedding-based models can reliably predict the direction of observed clinical outcomes across diverse therapeutic classes (e.g., small molecules, biologics), even when masking the observational data for the particular mechanism being estimated, though the precise magnitude of treatment effect remains hard to recover. This approach illustrates the potential to estimate the clinical efficacy of new drug mechanisms and to enhance the precision of future trial design and operations.