MSR11 A Step-By-Step Guide for Causal Study Design When Estimating Treatment Effects Using Real-World Data

Causal inference (CI) in observational research is growing more important, driven by the need for generalizable and rapidly delivered real-world evidence (RWE) to inform regulatory, payer, and patient/provider decision-making. Existing methodological literature on this topic is rich but can be complex and daunting to navigate. We describe a framework to approach these methods with confidence.