It can be used in social research, political science, and other areas. In the same way that a correlation does not imply a causation, it can also be said that a lack of correlation doesn't imply a lack of causation.
It has to work both ways. This might seem like a strange point to make. Many people assume that correlation is the minimum required, and then other forms of analyses have to be applied. But it's important to remember that truth is independent, and something doesn't become less truthful because of our inability to measure it. If something happened and a cause occurred, then it happened whether we have a way of measuring the causation or not. Just because you can't see a correlation, that doesn't mean that there wasn't some kind of causation at play.
It's vital not to fall into the trap of forgetting about this issue because it can be very important. How Should Causation Be Established? It's not easy to measure and establish causation, and there is no set path that will guarantee an easy way to test it. It all depends on the situation at hand and what kind of causal relationship needs to be tested.
Of course, you can't just assume that correlation implies causation; we've covered that already. But when you find correlation, it can be an indication to examine the situation further to determine if causation can be established between the variables.
Thorough testing and the elimination of variables that could impact findings can help test the hypothesis. If other factors can be eliminated that could be causing the appearance of correlation, the evidence for causation of the remaining variables could be strengthened.
Bradford Hill's criteria for causation will also help you to identify if a causal relationship is present. He lists nine criteria that help to identify causation. Hill's criteria for causation in biological research is strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy.
Now we have discussed correlation and causation and how they are related, we will discuss the different ways in which causation can be wrongly inferred from correlation. Reverse causation is as simple as its name. When you observe a correlation, it's possible to interpret it in the wrong way. Instead of seeing that A causes B, you might assume that B causes A. It's easy to get these things mixed up, but when you put it in simple terms and use a very basic and obvious example, you can see that the causation that you've identified is incorrect.
It's one of the most common ways to incorrectly infer causation from correlation. Consider how a solar panel works and we can see how reverse causation happens. When a solar panel generates more power, the sun is visible in the sky for longer. But that doesn't mean that the solar panel's increase in power generation causes the sun to stay in the sky for longer.
Instead, the reverse is actually true. The sun was visible in the sky for longer, and this is what led to the solar panel producing more power during that period of time.
The Common-Causal Variable. A principal aim of epidemiology is to assess the causes of disease. However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer that a cause-effect relationship exists. Specifically, causation needs to be distinguished from mere association — the link between two variables often an exposure and an outcome.
An observed association may in fact be due to the effects of one or more of the following:. For example, a study may find an association between using recreational drugs exposure and poor mental wellbeing outcome and thus conclude that using drugs is likely to impair wellbeing. A reverse causation explanation could be that people with poor mental wellbeing are more likely to use recreational drugs as, say, a means of escapism.
An observed statistical association between a risk factor and a disease does not necessarily lead us to infer a causal relationship; conversely, the absence of an association does not necessarily imply the absence of a causal relationship.
A judgment about whether an observed statistical association represents a cause-effect relationship between exposure and disease requires inferences far beyond the data from a single study.
The Bradford Hill criteria, listed below, are widely used in epidemiology as a framework with which to assess whether an observed association is likely to be causal. Although widely used, the criteria are not without criticism.
Rothman argues that Hill did not propose these criteria as a checklist for evaluating whether a reported association might be interpreted as causal, but they have been widely applied in this way.
He contends that the Bradford Hill criteria fail to deliver on the hope of clearly distinguishing causal from non-causal relations. For example, the first criterion 'strength of association' does not take into account the fact that not every component cause will have a strong association with the disease it produces, or that strength of association also depends on the prevalence of other factors.
In terms of the third criterion, 'specificity', which suggests that a relationship is more likely to be causal if the exposure is related to a single outcome, Rothman argues that this criterion is misleading as a cause may have many effects, for example smoking.
According to Rothman, the only criterion that can be considered as a true causal criterion is 'temporality', that is that the cause precedes the effect. It may be difficult, however, to ascertain the time sequence for cause and effect. So in practice, it becomes quite a challenge to make strong causal claims without controlling for unknown and unmeasured confounders.
Methods for tackling these problems are beyond the scope of this blog. Collider bias occurs when an exposure and outcome share a common effect the collider.
In this case, a distorted association between the exposure and the outcome is produced when we control for the collider, as illustrated in Figure 3.
Figure 3. Causal diagram illustrating the structure of collider bias. It is plausible that both joint trauma and knee osteoarthritis lead to surgical intervention, such as knee arthroscopy the collider.
That is, individuals who suffer a traumatic joint injury or those with a diagnosis of knee osteoarthritis are likely to undergo knee arthroscopy. In this case, if the collider knee arthroscopic surgery is controlled for by study design or analysis , we will observe a distorted association between joint trauma and knee osteoarthritis Figure 4. Figure 4. Causal diagram illustrating a distorted association between joint trauma and osteoarthritis by controlling for the collider, exposure to arthroscopic surgery.
Collider bias could be induced if, for instance, researchers only gain access to data from those who have undergone surgical intervention which would induce selection bias — a form of collider bias. Or if researchers have access to the entire dataset, but mistakenly decide to statistically control for surgical intervention during analysis. In effect, both mistakes will induce a biased association between joint trauma and knee osteoarthritis.
When we study a group of individuals who received surgery only as a result of joint trauma or knee osteoarthritis , knowing that a patient underwent surgery because of joint trauma will tell us that the patient is less likely to have knee osteoarthritis and vice versa. In other words, knee osteoarthritis becomes dependent on joint trauma within a sample of patients who undergo surgery even though they are independent in the wider population.
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