There is a time relationship between cause and effect in that the effect occurs after the cause. Also, if it is to be expected that there is some delay between cause and effect then that delay should also be observed.
Cause-and-effect may be observed by statistical correlation between these in repeated events or experiments. Full strength correlation has a coefficient of 1. A weaker association between cause and effect will see greater variation.
In treatment, there might be expected to be a relationship between the dose given and the reaction of the patient. This may not be a simple linear relationship and may have minimum and maximum thresholds.
One apparent success does not prove a general cause and effect in wider contexts. To prove a treatment is useful, it must give consistent results in a wide range of circumstances.
The apparent cause and effect must make sense in the light of current theories and results. If a causal relationship appears to be outside of current science then significant additional hypothesizing and testing will be required before a true cause and effect can be found.
A specific relationship is found if there is no other plausible explanation. This is not always the case in medicine where any given symptoms may have a range of possible causing conditions.
A very strong proof of cause and effect comes from the results of experiments, where many significant variables are held stable to prevent them interfering with the results. Other evidence is also useful but can be more difficult to isolate cause and effect.
When something is suspected of causing and effect, then other factors similar or analogous to the supposed cause should also be considered and identified as a possible cause or otherwise eliminated from the investigation.
If laboratory experiments in which variables are controlled and external everyday evidence are in alignment, then it is said that there is coherence.