Correlation, Causation, and Association – What Does It All Mean???

Correlation – When researchers find a correlation, which can also be called an association, what they are saying is that they found a relationship between two, or more, variables. For instance, in the case of the marijuana post (link is external), the researchers found an association between using marijuana as a teen, and having more troublesome relationships in mid, to late, twenties.

Correlations can be positive – so that as one variable (marijuana smoking) goes up, so does the other (relationship trouble); or they can be negative, which would mean that as one variable goes up (methamphetamine smoking) another goes down (grade point average). The trouble is that, unless they are properly controlled for, there could be other variables affecting this relationship that the researchers don’t know about. For instance, education, gender, and mental health issues could be behind the marijuana-relationship association (these variables were all controlled for by the researchers in that study).

Researchers have at their disposal a number of sophisticated statistical tools to control for these, ranging from the relatively simple (like multiple regression (link is external)) to the highly complex and involved (multi-level modeling (link is external) and structural equation modeling (link is external)). These methods allow researchers to separate the effect of one variable from others, thereby leaving them more confident in making assertions about the true nature of the relationships they found. Still, even under the best analysis circumstances, correlation is not the same as causation.

Causation – When an article says that causation was found, this means that the researchers found that changes in one variable they measured directly caused changes in the other. An example would be research showing that jumping of a cliff directly causes great physical damage. In order to do this, researchers would need to assign people to jump off a cliff (versus lets say jumping off of a 12 inch ledge) and measure the amount of physical damage caused. When they find that jumping off the cliff causes more damage, they can assert causality. Good luck recruiting for that study!

Most of the research you read about indicates a correlation between variables, not causation. You can find the key words by carefully reading. If the article says something like “men were found to have,” or “women were more likely to,” they’re talking about associations, not causation.

Why the difference?

The reason is that in order to actually be able to claim causation, the researchers have to split the participants into different groups, and assign them the behavior they want to study (like taking a new drug), while the rest don’t. This is in fact what happens in clinical trials of medication because the FDA requires proof that the medication actually makes people better (more so than a placebo). It’s this random assignment to conditions that makes experiments suitable for the discovery of causality. Unlike in association studies, random assignment assures (if everything is designed correctly) that its the behavior being studied, and not some other random effect, that is causing the outcome.

Obviously, it is much more difficult to prove causation than it is to prove an association.

Should we just ignore associations?

No! Not at all!!! Not even close!!! Correlations are crucial for research and still need to be looked at and studied, especially in some areas of research like addiction.

The reason is simple – We can’t randomly give people drugs like methamphetamine as children and study their brain development to see how the stuff affects them, that would be unethical. So what we’re left with is a the study of what meth use (and use of other drugs) is associated with. It’s for this reason that researchers use special statistical methods to assess associations, making certain that they are also considering other things that may be interfering with their results.

In the case of the marijuana article, the researchers ruled out a number of other interfering variables known to affect relationships, like aggression, gender, education, closeness with other family members, etc. By doing so, they did their best to assure that the association found between marijuana and relationship status was real. Obviously other possibilities exist, but as more researchers assess this relationship in different ways, we’ll learn more about its true nature.

This is how research works.

It’s also how we found out that smoking causes cancer. Through endlessly repeated findings showing an association. That turned out pretty well, I think…

Source: Psychology Today

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What “Causes” Disease?: Association vs. Causation and the Hill Criteria

Sara Gorman wrote . . . . .

Does cigarette smoking cause cancer? Does eating specific foods or working in certain locations cause diseases? Although we have determined beyond doubt that cigarette smoking causes cancer, questions of disease causality still challenge us because it is never a simple matter to distinguish mere association between two factors from an actual causal relationship between them. In an address to the Royal Society of Medicine in 1965, Sir Austin Bradford Hill attempted to codify the criteria for determining disease causality. An occupational physician, Hill was primarily concerned with the relationships among sickness, injury, and the conditions of work. What hazards do particular occupations pose? How might the conditions of a specific occupation cause specific disease outcomes?

In an engaging and at times humorous address, Hill delineates nine criteria for determining causality. He is quick to add that none of these criteria can be used independently and that even as a whole they do not represent an absolute method of determining causality. Nevertheless, they represent crucial considerations in any deliberation about the causes of disease, considerations that still resonate half a century later.

The criteria, which Hill calls “viewpoints,” are as follows:

1. Strength. The association between the projected cause and the effect must be strong. Hill uses the example of cigarette-smoking here, noting that “prospective inquiries have shown that the death rate from cancer of the lung in cigarette smokers is nine to ten times the rate in non-smokers.” Even when the effects are objectively small, if the association is strong, causality can be contemplated. For example, during London’s 1854 cholera outbreak, John Snow observed that the death rate of customers supplied with polluted drinking water from the Southwark and Vauxhall Company was low in absolute terms (71 deaths in 10,000 houses). Yet in comparison to the death rate in houses supplied with the pure water of the Lambeth Company (5 in 10,000), the association became significant. Even though the mechanism by which polluted water causes cholera—transmission of the bacteria vibrio cholera—was then still unknown, the strength of this association was sufficient in Snow’s mind to correctly assign a causal link.
2. Consistency. The effects must be repeatedly observed by different people, in different places, circumstances and times.
3. Specificity. Hill admits this is a weaker criterion, since diseases may have many causes and etiologies. Nevertheless, the specificity of the association, meaning how limited the association is to specific workers and sites and types of disease, must be taken into account in order to determine causality.
4. Temporality. Cause must precede effect.
5. Biological gradient. This criterion is also known as the dose-response curve. A good indicator of causality is whether, for example, death rates from cancer rise linearly with the number of cigarettes smoked. A small amount of exposure should result in a smaller effect. This is indeed the case; the more cigarettes a person smokes over a lifetime, the greater the risk of getting lung cancer.
6. Plausibility. The cause-and-effect relationship should be biologically plausible. It must not violate the known laws of science and biology.
7. Coherence. The cause-and-effect hypothesis should be in line with known facts and data about the biology and history of the disease in question.
8. Experiment. This would probably be the most important criterion if Hill had produced these “viewpoints” in 2012. Instead, Hill notes that “Occasionally it is possible to appeal to experimental, or semi-experimental, evidence.” An example of an informative experiment would be to take preventive action as a result of an observed association and see whether the preventive action actually reduces incidence of the disease.
9. Analogy. If one cause results in a specific effect then a similar cause can be said to result in a similar effect. Hill uses the example of thalidomide and rubella, noting that similar evidence with another drug and another viral disease in pregnancy might be accepted on analogy, even if the evidence is slighter.

The impact of Hill’s criteria has been enormous. They are still widely accepted in epidemiological research and have even spread beyond the scientific community. In this short yet captivating address, Hill managed to propose criteria that would constitute a crucial aspect of epidemiological research for decades to come. One wonders how Hill would respond to the plethora of reports published today claiming a cause and effect relationship between two factors based on an odds ratio of 1.2, with a statistically significant probability value of less than 0.05. While such an association may indeed be real, it is far smaller than those Hill discusses in his first criterion (“strength”). Hill does say, “We must not be too ready to dismiss a cause-and-effect hypothesis merely on the grounds that the observed association appears to be slight.” Yet he also wonders if “the pendulum has not swung too far” in substituting statistical probability testing for biological common sense. Claims that environmental exposures, food, chemicals, and types of stress cause a myriad of diseases pervade both scientific and popular literature today. In evaluating these issues, Hill’s sobering ideas, albeit 50 years old, are still useful guidance.

Source: Science Blogs