Evidence guide
Correlation vs. Causation
Two things moving together does not by itself show that changing one will change the other.
The confounding problem
A third factor can influence both exposure and outcome. Health, income, access, genetics and prior behavior often create plausible alternatives.
Reverse causation
The apparent outcome may actually influence the exposure. Early illness can reduce exercise, making inactivity appear to cause disease more strongly than it does.
When causal language is stronger
Randomization, natural experiments, clear timing, dose response, negative controls, triangulation and plausible mechanisms can strengthen—but not magically guarantee—causal inference.
A practical reading checklist
- Was the exposure assigned or observed?
- Did the cause clearly precede the outcome?
- Which confounders were measured?
- Do different methods point the same way?