Why can't causation be inferred from correlational studies?

Causation cannot be inferred from correlational studies because correlation does not imply causation.

Correlational studies are a type of research design where researchers measure two variables and assess the statistical relationship (correlation) between them with little or no effort to control extraneous variables. The key point to remember here is that correlational studies only show that two variables are related, not that one variable causes changes in another.

For example, let's say a study finds a correlation between eating ice cream and drowning incidents. It would be incorrect to infer from this that eating ice cream causes drowning. The correlation might be due to a third variable, such as hot weather, which increases both ice cream consumption and swimming activities, thereby increasing the risk of drowning. This is known as a confounding variable, an extraneous factor that affects both variables and can give the illusion of causation where there is none.

Moreover, correlation does not tell us about the direction of causation. This is known as the directionality problem. If two variables, say stress and poor sleep, are found to be correlated, we cannot determine if stress causes poor sleep, or if poor sleep causes stress, or if both are caused by another factor.

Lastly, there could be a chance factor involved. Sometimes, two variables might appear to be correlated, but the relationship is due to random chance. This is especially true in cases where multiple comparisons are made, increasing the likelihood of finding a significant correlation by chance alone.

In conclusion, while correlational studies are valuable for identifying relationships between variables, they cannot establish causation. To infer causation, experimental studies are needed where researchers manipulate one variable (the independent variable) and measure the effect on another variable (the dependent variable), while controlling for other potential influencing factors.