Rubin Causal Model

The Rubin Causal Model is a framework used to understand cause-and-effect relationships. It focuses on comparing potential outcomes: what would happen under different scenarios. For example, if we want to know if a new medication is effective, we compare the health outcomes of people who receive the medication with those who do not. The model emphasizes the importance of randomization in experiments to reduce bias, ensuring that any observed effects can be attributed to the treatment rather than other factors. This approach helps researchers draw clearer conclusions about causality.