Causality refers to a fixed relationship between cause and effect. Causality means that an event (cause) produces another event (effect), whereby the mechanism behind it is known (scientific explanation).

Example: A car is parked on a steep road without the handbrake (cause X), starts to roll and smashes into a garden fence (result Y). Result Y is therefore the direct consequence of cause X.

Careful investigations are necessary to scientifically establish causality. There is a risk that a relationship between two variables can be attributed to correlation, coincidence or the occurrence of third variables. The principles of causality and correlation are often confused or equated. Confusing causality and correlation harbours the risk of drawing false conclusions. This can have far-reaching consequences for decision-making processes in science, business and society. Just because there is a correlation between two variables does not automatically mean that the cause-and-effect relationship is causal.

The following example explains the difference between causality and correlation:
The number of storks in Switzerland decreased over decades at the same time as the fertility of the population (number of babies measured against the population size). It would be wrong to conclude from this that the decline in the human birth rate is due to the decline in the stork population. Instead, both results can be causally explained by other causes that occurred at the same time: The industrialisation-induced increase in prosperity has led to smaller family sizes. At the same time, the drainage and overbuilding of many lowland areas has led to a reduction in the habitat and food supply for storks.

For a decision-making process, it is essential to analyse the actual causal relationships.