Empirical methods in statistics and causal inference, such as Granger causality and directed acyclic graphs, allow researchers to distinguish true causal relationships from mere observed regularities in observational data.
The retrieved papers consistently demonstrate that advanced statistical frameworks, causal discovery algorithms, and observational study designs (such as Granger causality or graphical models) are specifically built and utilized to distinguish true causal mechanisms from mere correlation or observed regularity.