False Causality

We are always in search of patterns. The tendency means that sometimes we even find patterns where none really even exist. Our brains are so trained in this way that we will even make sense of chaos to the extent that we can.

Because our training wires us to seek out patterns, it’s crucial to remember the simple maxim that correlation does not imply causation. Just because two variables move in tandem doesn’t necessarily mean that one causes the other.

This principle has been hilariously demonstrated by numerous examples. For instance, by looking at fire department data, you notice that, as more firemen are dispatched to a fire, the more damage is ultimately done to a property. Thus, you might infer that more firemen are causing more damage. In another famous example, an academic who was investigating the cause of crime in New York City in the 1980s found a strong correlation between the number of serious crimes committed and the amount of ice cream sold by street vendors. But should we conclude that eating ice cream drives people to crime? Since this makes little sense, we should obviously suspect that there was an unobserved variable causing both. During the summer, crime rates are the highest, and this is also when most ice cream is sold. Ice cream sales don’t cause crime, nor does crime increase ice cream sales. In both of these instances, looking at the data too superficially leads to incorrect assumptions.

Rahul Agarwal writing in Built in