Causal inference can be conceptualized as a “double helix of causal thinking,” intertwining data and reality through two fundamental principles. The first strand is the Law of Counterfactuals: What would have happened had circumstances been different? In rare disease, this means asking whether a specific patient would have benefited without treatment, whether another intervention would have produced a better outcome, or whether a therapy that appears successful on average may fail for a particular individual. The second strand is the Law of Conditional Independence: How can we determine whether our assumptions about cause and effect are reflected in the data? This principle enables researchers to use causal diagrams and structural models to identify treatment effects, even when data are limited and conventional statistical methods reach their limits. Together, these principles offer a path toward faster, less expensive, and more precise drug development by combining randomized and observational evidence, identifying likely responders before approval, leveraging real-world data more effectively, and estimating individual treatment effects rather than relying solely on population averages. Causal inference is an essential step beyond the Plausible Mechanism Framework (5) response to precision medicine regulation. The randomized clinical trial remains indispensable, but without causal inference, it remains largely confined to providing answers based on averages of large populations. Rare disease and personalized medicine requires the ability to reason about causes, interventions, and counterfactual outcomes. In that sense, causal inference is not merely another analytical tool. It is the scientific framework that can connect data to reality and make truly individualized treatment decisions possible.