But in order for A to be a cause of B they must be associated in some way. Meaning there is a correlation between them - though that correlation does not necessarily need to be linear. As some of the commenters have suggested, it's likely more appropriate to use a term like 'dependence' or 'association' rather than correlation.
Though as I've mentioned in the comments, I've seen "correlation does not mean causation" in response to analysis far beyond simple linear correlation, and so for the purposes of the saying, I've essentially extended "correlation" to any association between A and B. Adding to EpiGrad 's answer. I think, for a lot of people, "correlation" will imply "linear correlation". And the concept of nonlinear correlation might not be intuitive.
So, I would say "no they don't have to be correlated but they do have to be related ". We are agreeing on the substance, but disagreeing on the best way to get the substance across. One example of such a causation at least people think it's causal is that between the likelihood of answering your phone and income.
It is known that people at both ends of the income spectrum are less likely to answer their phones than people in the middle. It is thought that the causal pattern is different for the poor e.
Things are definitely nuanced here. The cause and the effect will be correlated unless there is no variation at all in the incidence and magnitude of the cause and no variation at all in its causal force. The only other possibility would be if the cause is perfectly correlated with another causal variable with exactly the opposite effect.
Basically, these are thought-experiment conditions. In the real world, causation will imply dependence in some form although it might not be linear correlation.
There are great answers here. Artem Kaznatcheev , Fomite and Peter Flom point out that causation would usually imply dependence rather than linear correlation.
Carlos Cinelli gives an example where there's no dependence, because of how the generating function is set up. I want to add a point about how this dependence can disappear in practice, in the kinds of datasets that you might well work with.
Situations like Carlos's example are not limited to mere "thought-experiment conditions". Dependences vanish in self-regulating processes. Homeostasis, for example, ensures that your internal body temperature remains independent of the room temperature. External heat influences your body temperature directly, but it also influences the body's cooling systems e. If we sample temperature in extremely fast intervals and using extremely precise measurements, we have a chance of observing the causal dependences, but at normal sampling rates, body temperature and external temperature appear independent.
Self-regulating processes are common in biological systems; they are produced by evolution. Mammals that fail to regulate their body temperature are removed by natural selection.
Researchers who work with biological data should be aware that causal dependences may vanish in their datasets. Unless, like the accepted answer implies, you're using an incredibly limited interpretation of the word 'correlation', it's a silly question- if one thing 'causes' another, it is by definition affected by it in some way, whether it's an increase in population, or just intensity. That's the more likely cause of the increased crime. Second, you might be dealing with reverse causation.
This happens when, instead of correctly assuming that A causes B, you get them mixed up and assume that B causes A. It might be hard to imagine how this happens, but think of how solar panels work. They produce more power when the sun is in the sky longer. But the sun isn't in the sky longer because the panels are producing more power. The panels are producing more power because the sun shines for longer periods of time.
Third, we must not forget the power of coincidence. When two things happen to occur at the same time, it's tempting to see causation. But just like that silly graph above, with the arcades and CS degrees, many are just coincidences. Perhaps you're trying to figure out whether a certain new drug makes patients feel better. Or you'd like to know what makes people buy a certain product. Whatever your motivation, it's often very useful to figure out whether A causes B, along with how and why.
But as we've seen, it's not that easy. You've got to control as many factors as you can, reduce the likelihood of confounding variables and coincidences, and pare down the data to what's relevant.
We won't get into the deeper philosophical question of how we can really establish causation without a doubt. That's for another time. At least now you know that - even though two events or variables may seem related - it doesn't mean that one has a direct causal affect on the other.
If you read this far, tweet to the author to show them you care. Tweet a thanks. Determining causality is never perfect in the real world. However, there are a variety of experimental, statistical and research design techniques for finding evidence toward causal relationships: e. Beyond the intrinsic limitations of correlation tests e. For example, imagine again that we are health researchers, this time looking at a large dataset of disease rates, diet and other health behaviors.
Suppose that we find two correlations: increased heart disease is correlated with higher fat diets a positive correlation , and increased exercise is correlated with less heart disease a negative correlation. Both of these correlations are large, and we find them reliably. Surely this provides a clue to causation, right?
In the case of this health data, correlation might suggest an underlying causal relationship, but without further work it does not establish it. Imagine that after finding these correlations, as a next step, we design a biological study which examines the ways that the body absorbs fat, and how this impacts the heart.
Perhaps we find a mechanism through which higher fat consumption is stored in a way that leads to a specific strain on the heart. My hypothesis is that there's no evidence to support a causal relationship between these two variables. While this example from Tyler's website seems extreme, it's poking fun at how people can immediately visualize a relationship between two numerical variables and naively jump to the conclusion that there's a causal relationship.
The joke is that the guy on the right feels he doesn't have strong evidence such as through a study to prove his statistics class caused him to believe that fact is true. A mediator variable is a variable that explains the relationship between independent and dependent variables. For example, we may notice a positive correlation with increased ice cream shop sales with increased heat. However, a potential mediator variable could be the count of people sweating.
It's possible an increase in the count of people sweating in the local area influences ice cream sales. If this were true, you may want to open an ice cream store near a sauna rather than simply in a hot weather area. To make a causal relationship, we need to rule out lurking variables. These are variables that are not included in the independent or dependent variable but can affect the relationship between the two.
The definition of the mediator variable above is considered a lurking variable too.
0コメント