Advancements in causal inference methods have improved our ability to determine cause-and-effect relationships in epidemiological studies. Techniques such as propensity score matching, instrumental variable analysis, and Mendelian randomization help control for confounding variables and biases, leading to more robust and reliable conclusions about disease causation and intervention effectiveness.
