There are many factors that influence our tendency to overstate causality or imply it where it does not exist. For example, when someone goes on a shooting rampage, people want to know why (i.e., they want a cause for the behavior). When someone makes a million dollars, people want to how (i.e., they want a cause for the success). The problem is, causality as a concept is misunderstood and oversimplified.
While a strong understanding of causality can help individuals and society address real problems rather than imagined ones, understanding is only part of the problem. Causality is often assumed where it doesn't exist for several reasons, some of which include black and white thinking (e.g., something is either a cause or it is not, rather than being a partial cause), the need for closure (e.g., accepting any cause over no known cause), ignorance of the many influences, and bias or self-interest.
In research, there are three parts to demonstrating causality which often go by the unnecessarily complex terms (1) demonstrating covariation, (2) eliminating spurious relations, and (3) establishing the time order of the occurrences. These rules can be applied to everyday situations, as well.
When two or more phenomena are said to covary
or are said to be correlated
, it means that they change together. This does not necessarily mean, however, that one phenomenon caused the other(s). If we suspect that a shooting rampage was caused by psychopathy (i.e., the shooter being a psychopath), do we see more violent behavior in psychopaths than in non-psychopaths? We do (Skeem, Polaschek, Patrick, & Lilienfeld, 2011), but it is not a perfect correlation, meaning that there are many non-violent psychopaths as well. Because it is an imperfect correlation (all are when we are dealing with something as complex as human behavior), we can more precisely say that psychopathy was an influence
or a factor
in the behavior. Many times we can estimate how much of a factor using one of many statistical methods.
Eliminating Spurious Relations
A spurious relation
is a non-causal relationship between two variables or events that appear to be have a causal relationship because they change together (covary). Gun ownership is strongly correlated with shootings rampages. Does this mean that gun ownership is the cause of these shooting rampages? Part of the critical thinking process in attempting to establish causality is asking the question "what else can be going on here?" In this example, a predisposition to violence is the confounding variable
, or variable that is correlated to the other two variables that can also explain the effect. In other words, people with a predisposition to violence are both more likely to own guns and go on shooting sprees. This spurious relationship puts our "gun ownership" hypothesis into question.
Establishing Time Order
We would need to demonstrate that the assumed cause occurs before the assumed effect. In our shooting spree example, this is pretty straight forward, since a shooting spree is often the final event in a shooter's life. But what about the cause of someone's success? People often credit their self-confidence for their success, but they often forget that their self-confidence was a result of their success, in other words, it came after their success.
In social science and when dealing with human behavior, causality is established in probabilistic terms and rarely used. We don't say that a person went on a shooting spree because
they were a psychopath; we say that a person's psychopathy was a likely significant contributor
to that person's shooting spree. We don't say that persistence causes success; we say that persistence plays a role
in success. Using more precise language leaves the door open for discovery and communicates a far more accurate picture of reality.The scientist's motto: Correlation Does Not Equal Causation!
Skeem, J. L., Polaschek, D. L. L., Patrick, C. J., & Lilienfeld, S. O. (2011). Psychopathic personality bridging the gap between scientific evidence and public policy. Psychological Science in the Public Interest, 12(3), 95–162. doi:10.1177/1529100611426706
Bo Bennett, PhD
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