The Rubin Causal Model (RCM) is a framework used in statistics to understand causal relationships. It focuses on comparing potential outcomes for individuals under different conditions, such as treatment versus no treatment. By considering what would happen to the same individual in both scenarios, researchers can better estimate the effect of an intervention. In RCM, the concept of the "counterfactual" is crucial. This refers to the outcome that would have occurred if a different action had been taken. The model helps in designing experiments and observational studies to draw valid conclusions about causality, minimizing biases and confounding factors.