relationships in final models:

First, consider if the data you are analyzing are from a clinical trial or cohort study. If they are,

then you will have met the criterion of temporality, which means the exposure or intervention

preceded the outcome and is especially strong evidence for causation.

If the estimate for the exposure in your regression model is large, you can say you have a strong

magnitude of association, and this is evidence of causation. This is especially true if your estimate

is larger than those of the confounders in the model as well as similar estimates from the scientific

literature.

If your exposure shows a dose-response relationship with the outcome, it is evidence of causation.

In other words, if your regression model shows that the more individuals smoke, the higher their

risk for lung cancer, this is evidence of causation (see Chapter 18 for more on dose-response

relationships).

If the estimate is consistent in size and direction with other analyses, including previous studies

you’ve done and studies in the scientific literature, there is more evidence for causation.