What does a causal mechanism explain?

A causal mechanism explains the logical linkage between an independent variable and a dependent variable, which is fundamental to understanding how one variable can influence another. This concept goes beyond merely identifying a relationship; it delves into the processes and pathways through which the independent variable leads to changes in the dependent variable.

For example, if researchers find that increased exercise (independent variable) leads to weight loss (dependent variable), the causal mechanism would explore how exercise affects metabolism, energy expenditure, and fat oxidation, providing a clear understanding of the underlying processes. This helps to clarify not just that there is an association, but why and how one variable affects the other.

Other options might highlight aspects of relationships between variables, but they do not encapsulate the depth of causal mechanisms. The general relationship between two variables might indicate correlation, yet it does not detail how one influences the other. Merely talking about correlation without addressing causation overlooks the process involved. Discussing statistical significance pertains to determining whether the findings are likely due to chance, rather than explaining the causal processes at play. Thus, focusing on the logical linkage captures the essence of what a causal mechanism is meant to convey.