Causal inference is needed when you want to answer a research question involving causation (e.g., does X cause Y); otherwise, we are only able to establish a relationship (that is, association). Causal inference is challenging when “real-world” observational data are used, because without randomization, there may be other factors that could drive the differences observed between treatment groups.
Generating a strong body of evidence regarding a product requires both randomized trials and real-world data. Trials are limited in their patient diversity both clinically and demographically as well as in their follow-up time. Real-world, observational studies of product safety are typically required by regulatory agencies upon approval of a new product; moreover, manufacturers – as well as patients, providers, and payers – may want to know if new products are providing the expected health benefits and how cost of care is affected.