Question

Explain the difference between causation due to correlation and causation that isn't caused by correlation

Answer 1

Causation or causality as we call it can be defined as establishing a cause and effect relationship among two events or variables. Causation arises when say two events A and b occurring together or one after the other are responsible for their occurrence. One can say that low visibility and more accidents are the cause of mishap. Since the visibility was low (Event A), the accident rate increased (Event B). Here the two events have a cause and effect relationship. Now if this causation creates an impression that there is correlation between visibility and accidents, then it will be called causation due to correlation. Collection and analysis of big data sets available today are a good example of causation due to correlation. If statistically proven then we can say that it is causation due to correlation.

However the truth is that correlation and causation are two different things. They should not be confused with each other. Either the events are causation or they have a correlation. Causation does not mean that two events have a correlation. Seat belt and injuries can be seen as a causal relation where one may find more injuries amongst people without seat belt as compared to lower or no injuries with people wearing a seat belt. But this just a causal relationship there may be other reasons also for injuries. Here causation is not caused by correlation, it is only a chance event.

Hence we can have both causation due to correlation and causation that isn't caused by correlation but they differ significantly from each other.