Correlation does not imply causation

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The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. [1] [2] The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc ('with this, therefore because of this'). This differs from the fallacy known as post hoc ergo propter hoc ("after this, therefore because of this"), in which an event following another is seen as a necessary consequence of the former event, and from conflation, the errant merging of two events, ideas, databases, etc., into one.

Contents

As with any logical fallacy, identifying that the reasoning behind an argument is flawed does not necessarily imply that the resulting conclusion is false. Statistical methods have been proposed that use correlation as the basis for hypothesis tests for causality, including the Granger causality test and convergent cross mapping. The Bradford Hill criteria, also known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship.

Usage and meaning of terms

"Imply"

In casual use, the word "implies" loosely means suggests, rather than requires. However, in logic, the technical use of the word "implies" means "is a sufficient condition for." [3] That is the meaning intended by statisticians when they say causation is not certain. Indeed, p implies q has the technical meaning of the material conditional: if p then q symbolized as p → q. That is, "if circumstance p is true, then q follows." In that sense, it is always correct to say "Correlation does not imply causation."

"Cause"

The word "cause" (or "causation") has multiple meanings in English. In philosophical terminology, "cause" can refer to necessary, sufficient, or contributing causes. In examining correlation, "cause" is most often used to mean "one contributing cause" (but not necessarily the only contributing cause).

Dinosaur illiteracy and extinction may be correlated, but that would not mean the variables had a causal relationship. Illiterate Dinos.jpg
Dinosaur illiteracy and extinction may be correlated, but that would not mean the variables had a causal relationship.

Causal analysis

This section is an excerpt from Causal analysis.[ edit ]
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [4] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments. [5]

Examples of illogically inferring causation from correlation

B causes A (reverse causation or reverse causality)

Reverse causation or reverse causality or wrong direction is an informal fallacy of questionable cause where cause and effect are reversed. The cause is said to be the effect and vice versa.

Example 1
The faster that windmills are observed to rotate, the more wind is observed.
Therefore, wind is caused by the rotation of windmills. (Or, simply put: windmills, as their name indicates, are machines used to produce wind.)

In this example, the correlation (simultaneity) between windmill activity and wind velocity does not imply that wind is caused by windmills. It is rather the other way around, as suggested by the fact that wind does not need windmills to exist, while windmills need wind to rotate. Wind can be observed in places where there are no windmills or non-rotating windmills—and there are good reasons to believe that wind existed before the invention of windmills.

Example 2
Low cholesterol is associated with an increase in mortality.
Therefore, low cholesterol increases your risk of mortality.

Causality is actually the other way around, since some diseases, such as cancer, cause low cholesterol due to a myriad of factors, such as weight loss, and they also cause an increase in mortality. [6] This can also be seen in alcoholics.[ citation needed ] As alcoholics become diagnosed with cirrhosis of the liver, many quit drinking. However, they also experience an increased risk of mortality. In these instances, it is the diseases that cause an increased risk of mortality, but the increased mortality is attributed to the beneficial effects that follow the diagnosis, making healthy changes look unhealthy.

Example 3

In other cases it may simply be unclear which is the cause and which is the effect. For example:

Children that watch a lot of TV are the most violent. Clearly, TV makes children more violent.

This could easily be the other way round; that is, violent children like watching more TV than less violent ones.

Example 4

A correlation between recreational drug use and psychiatric disorders might be either way around: perhaps the drugs cause the disorders, or perhaps people use drugs to self medicate for preexisting conditions. Gateway drug theory may argue that marijuana usage leads to usage of harder drugs, but hard drug usage may lead to marijuana usage (see also confusion of the inverse ). Indeed, in the social sciences where controlled experiments often cannot be used to discern the direction of causation, this fallacy can fuel long-standing scientific arguments. One such example can be found in education economics, between the screening/signaling and human capital models: it could either be that having innate ability enables one to complete an education, or that completing an education builds one's ability.

Example 5

A historical example of this is that Europeans in the Middle Ages believed that lice were beneficial to health since there would rarely be any lice on sick people. The reasoning was that the people got sick because the lice left. The real reason however is that lice are extremely sensitive to body temperature. A small increase of body temperature, such as in a fever, makes the lice look for another host. The medical thermometer had not yet been invented and so that increase in temperature was rarely noticed. Noticeable symptoms came later, which gave the impression that the lice had left before the person became sick. [7]

In other cases, two phenomena can each be a partial cause of the other; consider poverty and lack of education, or procrastination and poor self-esteem. One making an argument based on these two phenomena must however be careful to avoid the fallacy of circular cause and consequence. Poverty is a cause of lack of education, but it is not the sole cause, and vice versa.

Third factor C (the common-causal variable) causes both A and B

The third-cause fallacy (also known as ignoring a common cause [8] or questionable cause [8] ) is a logical fallacy in which a spurious relationship is confused for causation. It asserts that X causes Y when in reality, both X and Y are caused by Z. It is a variation on the post hoc ergo propter hoc fallacy and a member of the questionable cause group of fallacies.

All of those examples deal with a lurking variable, which is simply a hidden third variable that affects both of the variables observed to be correlated. That third variable is also known as a confounding variable, with the slight difference that confounding variables need not be hidden and may thus be corrected for in an analysis. Note that the Wikipedia link to lurking variable redirects to confounding. A difficulty often also arises where the third factor, though fundamentally different from A and B, is so closely related to A and/or B as to be confused with them or very difficult to scientifically disentangle from them (see Example 4).

Example 1
Sleeping with one's shoes on is strongly correlated with waking up with a headache.
Therefore, sleeping with one's shoes on causes headache.

The above example commits the correlation-implies-causation fallacy, as it prematurely concludes that sleeping with one's shoes on causes headache. A more plausible explanation is that both are caused by a third factor, in this case going to bed drunk, which thereby gives rise to a correlation. So the conclusion is false.

Example 2
Young children who sleep with the light on are much more likely to develop myopia in later life.
Therefore, sleeping with the light on causes myopia.

This is a scientific example that resulted from a study at the University of Pennsylvania Medical Center. Published in the May 13, 1999, issue of Nature , [9] the study received much coverage at the time in the popular press. [10] However, a later study at Ohio State University did not find that infants sleeping with the light on caused the development of myopia. It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom. [11] [12] [13] [14] In this case, the cause of both conditions is parental myopia, and the above-stated conclusion is false.

Example 3
As ice cream sales increase, the rate of drowning deaths increases sharply.
Therefore, ice cream consumption causes drowning.

This example fails to recognize the importance of time of year and temperature to ice cream sales. Ice cream is sold during the hot summer months at a much greater rate than during colder times, and it is during these hot summer months that people are more likely to engage in activities involving water, such as swimming. The increased drowning deaths are simply caused by more exposure to water-based activities, not ice cream. The stated conclusion is false.

Example 4
A hypothetical study shows a relationship between test anxiety scores and shyness scores, with a statistical r value (strength of correlation) of +.59. [15]
Therefore, it may be simply concluded that shyness, in some part, causally influences test anxiety.

However, as encountered in many psychological studies, another variable, a "self-consciousness score", is discovered that has a sharper correlation (+.73) with shyness. This suggests a possible "third variable" problem, however, when three such closely related measures are found, it further suggests that each may have bidirectional tendencies (see "bidirectional variable", above), being a cluster of correlated values each influencing one another to some extent. Therefore, the simple conclusion above may be false.

Example 5
Since the 1950s, both the atmospheric CO2 level and obesity levels have increased sharply.
Hence, atmospheric CO2 causes obesity.

Richer populations tend to eat more food and produce more CO2.

Example 6
HDL ("good") cholesterol is negatively correlated with incidence of heart attack.
Therefore, taking medication to raise HDL decreases the chance of having a heart attack.

Further research [16] has called this conclusion into question. Instead, it may be that other underlying factors, like genes, diet and exercise, affect both HDL levels and the likelihood of having a heart attack; it is possible that medicines may affect the directly measurable factor, HDL levels, without affecting the chance of heart attack.

Bidirectional causation: A causes B, and B causes A

Causality is not necessarily one-way;[ dubious – discuss ] in a predator-prey relationship, predator numbers affect prey numbers, but prey numbers, i.e. food supply, also affect predator numbers. Another well-known example is that cyclists have a lower Body Mass Index than people who do not cycle. This is often explained by assuming that cycling increases physical activity levels and therefore decreases BMI. Because results from prospective studies on people who increase their bicycle use show a smaller effect on BMI than cross-sectional studies, there may be some reverse causality as well. For example, people with a lower BMI may be more likely to want to cycle in the first place. [17]

The relationship between A and B is coincidental

The two variables are not related at all, but correlate by chance. The more things are examined, the more likely it is that two unrelated variables will appear to be related. For example:

Use of correlation as scientific evidence

Much of scientific evidence is based upon a correlation of variables [18] that are observed to occur together. Scientists are careful to point out that correlation does not necessarily mean causation. The assumption that A causes B simply because A correlates with B is not accepted as a legitimate form of argument.

However, sometimes people commit the opposite fallacy of dismissing correlation entirely. That would dismiss a large swath of important scientific evidence. [18] Since it may be difficult or ethically impossible to run controlled double-blind studies to address certain questions, correlational evidence from several different angles may be useful for prediction despite failing to provide evidence for causation. For example, social workers might be interested in knowing how child abuse relates to academic performance. Although it would be unethical to perform an experiment in which children are randomly assigned to receive or not receive abuse, researchers can look at existing groups using a non-experimental correlational design. If in fact a negative correlation exists between abuse and academic performance, researchers could potentially use this knowledge of a statistical correlation to make predictions about children outside the study who experience abuse even though the study failed to provide causal evidence that abuse decreases academic performance. [19] The combination of limited available methodologies with the dismissing correlation fallacy has on occasion been used to counter a scientific finding. For example, the tobacco industry has historically relied on a dismissal of correlational evidence to reject a link between tobacco smoke and lung cancer, [20] as did biologist and statistician Ronald Fisher (frequently on the industry's behalf). [list 1]

Correlation is a valuable type of scientific evidence in fields such as medicine, psychology, and sociology. Correlations must first be confirmed as real, and every possible causative relationship must then be systematically explored. In the end, correlation alone cannot be used as evidence for a cause-and-effect relationship between a treatment and benefit, a risk factor and a disease, or a social or economic factor and various outcomes. It is one of the most abused types of evidence because it is easy and even tempting to come to premature conclusions based upon the preliminary appearance of a correlation. [20]

See also

Related Research Articles

Causality is an influence by which one event, process, state, or object (acause) contributes to the production of another event, process, state, or object (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. In general, a process can have multiple causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space.

Post hoc ergo propter hoc is an informal fallacy which one commits when one reasons, "Since event Y followed event X, event Y must have been caused by event X." It is a fallacy in which an event is presumed to have been caused by a closely preceding event merely on the grounds of temporal succession. This type of reasoning is fallacious because mere temporal succession does not establish a causal connection. It is often shortened simply to post hoc fallacy. A logical fallacy of the questionable cause variety, it is subtly different from the fallacy cum hoc ergo propter hoc, in which two events occur simultaneously or the chronological ordering is insignificant or unknown. Post hoc is a logical fallacy in which one event seems to be the cause of a later event because it occurred earlier.

The questionable cause—also known as causal fallacy, false cause, or non causa pro causa —is a category of informal fallacies in which the cause or causes is/are incorrectly identified. In other words, it is a fallacy of reaching a conclusion that one thing caused another, simply because they are regularly associated.

<span class="mw-page-title-main">Spurious relationship</span> Apparent, but false, correlation between causally-independent variables

In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor.

The fallacy of the single cause, also known as complex cause, causal oversimplification, causal reductionism, root cause fallacy, and reduction fallacy, is an informal fallacy of questionable cause that occurs when it is assumed that there is a single, simple cause of an outcome when in reality it may have been caused by a number of only jointly sufficient causes.

The Profit Impact of Market Strategy (PIMS) program is a project that uses empirical data to try to determine which business strategies make the difference between success and failure. It is used to develop strategies for resource allocation and marketing. Some of the most important strategic metrics are market share, product quality, investment intensity, and service quality. One of the emphasized principles is that the same factors work identically across different industries.

<span class="mw-page-title-main">Granger causality</span> Statistical hypothesis test for forecasting

The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969. Ordinarily, regressions reflect "mere" correlations, but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. Since the question of "true causality" is deeply philosophical, and because of the post hoc ergo propter hoc fallacy of assuming that one thing preceding another can be used as a proof of causation, econometricians assert that the Granger test finds only "predictive causality". Using the term "causality" alone is a misnomer, as Granger-causality is better described as "precedence", or, as Granger himself later claimed in 1977, "temporally related". Rather than testing whether Xcauses Y, the Granger causality tests whether X forecastsY.

<span class="mw-page-title-main">Structural equation modeling</span> Form of causal modeling that fit networks of constructs to data

Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself.

<span class="mw-page-title-main">Causal model</span> Conceptual model in philosophy of science

In metaphysics, a causal model is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.

<span class="mw-page-title-main">Preston curve</span>

The Preston curve is an empirical cross-sectional relationship between life expectancy and real per capita income. It is named after Samuel H. Preston who first described it in 1975. Preston studied the relationship for the 1900s, 1930s and the 1960s and found it held for each of the three decades. More recent work has updated this research.

<span class="mw-page-title-main">Mendelian randomization</span> Statistical method in genetic epidemiology

In epidemiology, Mendelian randomization is a method using measured variation in genes to examine the causal effect of an exposure on an outcome. Under key assumptions, the design reduces both reverse causation and confounding, which often substantially impede or mislead the interpretation of results from epidemiological studies.

Probabilistic causation is a concept in a group of philosophical theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes raise the probabilities of their effects, all else being equal.

Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality extends from ancient philosophy to contemporary neuropsychology; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one. The first known protoscientific study of cause and effect occurred in Aristotle's Physics. Causal inference is an example of causal reasoning.

Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.

The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research. They were established in 1965 by the English epidemiologist Sir Austin Bradford Hill.

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

Convergent cross mapping (CCM) is a statistical test for a cause-and-effect relationship between two variables that, like the Granger causality test, seeks to resolve the problem that correlation does not imply causation. While Granger causality is best suited for purely stochastic systems where the influences of the causal variables are separable, CCM is based on the theory of dynamical systems and can be applied to systems where causal variables have synergistic effects. As such, CCM is specifically aimed to identify linkage between variables that can appear uncorrelated with each other.

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process.

Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis

<i>The Book of Why</i> 2018 book by Judea Pearl and Dana Mackenzie

The Book of Why: The New Science of Cause and Effect is a 2018 nonfiction book by computer scientist Judea Pearl and writer Dana Mackenzie. The book explores the subject of causality and causal inference from statistical and philosophical points of view for a general audience.

References

  1. ↑ Tufte 2006 , p. 5
  2. ↑ Aldrich, John (1995). "Correlations Genuine and Spurious in Pearson and Yule" (PDF). Statistical Science. 10 (4): 364–376. doi: 10.1214/ss/1177009870 . JSTOR   2246135.
  3. ↑ "Sufficient". Wolfram. 2019-12-02. Retrieved 2019-12-03.
  4. ↑ Rohlfing, Ingo; Schneider, Carsten Q. (2018). "A Unifying Framework for Causal Analysis in Set-Theoretic Multimethod Research" (PDF). Sociological Methods & Research. 47 (1): 37–63. doi:10.1177/0049124115626170. S2CID   124804330 . Retrieved 29 February 2020.
  5. ↑ Brady, Henry E. (7 July 2011). "Causation and Explanation in Social Science". The Oxford Handbook of Political Science. doi:10.1093/oxfordhb/9780199604456.013.0049 . Retrieved 29 February 2020.
  6. ↑ Naveed Sattar; David Preiss (13 Jun 2017). "Reverse Causality in Cardiovascular Epidemiological Research". Circulation. 135 (24): 2369–2372. doi: 10.1161/CIRCULATIONAHA.117.028307 . PMID   28606949.
  7. ↑ Willingham, Emily. "Of lice and men: An itchy history". Scientific American Blog Network. Retrieved 2019-02-26.
  8. 1 2 Labossiere, M.C., Dr. LaBossiere's Philosophy Pages Archived 2009-05-22 at the Wayback Machine
  9. ↑ Quinn, Graham E.; Shin, Chai H.; Maguire, Maureen G.; Stone, Richard A. (May 1999). "Myopia and ambient lighting at night". Nature. 399 (6732): 113–114. Bibcode:1999Natur.399..113Q. doi:10.1038/20094. PMID   10335839. S2CID   4419645.
  10. ↑ CNN, May 13, 1999. Night-light may lead to nearsightedness
  11. ↑ Ohio State University Research News, March 9, 2000. Night lights don't lead to nearsightedness, study suggests Archived 2006-09-01 at the Wayback Machine
  12. ↑ Zadnik, Karla; Jones, Lisa A.; Irvin, Brett C.; Kleinstein, Robert N.; Manny, Ruth E.; Shin, Julie A.; Mutti, Donald O. (2000). "Vision: Myopia and ambient night-time lighting". Nature. 404 (6774): 143–144. Bibcode:2000Natur.404..143Z. doi:10.1038/35004661. PMID   10724157. S2CID   4399332.
  13. ↑ Gwiazda, J.; Ong, E.; Held, R.; Thorn, F. (2000). "Vision: Myopia and ambient night-time lighting". Nature. 404 (6774): 144. Bibcode:2000Natur.404..144G. doi: 10.1038/35004663 . PMID   10724158.
  14. ↑ Stone, Richard A.; Maguire, Maureen G.; Quinn, Graham E. (2000). "Vision: reply: Myopia and ambient night-time lighting". Nature. 404 (6774): 144. Bibcode:2000Natur.404..144S. doi: 10.1038/35004665 . PMID   10724158.
  15. ↑ Carducci, Bernardo J. (2009). The Psychology of Personality: Viewpoints, Research, and Applications (2nd ed.). John Wiley & Sons. ISBN   978-1-4051-3635-8.
  16. ↑ Ornish, Dean. "Cholesterol: The good, the bad, and the truth" (retrieved 3 June 2011)
  17. ↑ Dons, E (2018). "Transport mode choice and body mass index: Cross-sectional and longitudinal evidence from a European-wide study" (PDF). Environment International. 119 (119): 109–116. doi:10.1016/j.envint.2018.06.023. hdl: 10044/1/61061 . PMID   29957352. S2CID   49607716.
  18. 1 2 Novella (18 November 2009). "Evidence in Medicine: Correlation and Causation". Science and Medicine. Science-Based Medicine.
  19. ↑ Nielsen, Michael (2012-01-23). "If correlation doesn't imply causation, then what does? | DDI". Michaelnielsen.org. Retrieved 2017-10-08.
  20. 1 2 "Evidence in Medicine: Correlation and Causation – Science-Based Medicine". Sciencebasedmedicine.org. 2009-11-18. Retrieved 2017-10-08.
  21. ↑ Silver, Nate (2015), The Signal and the Noise: Why So Many Predictions Fail – But Some Don't (2nd ed.), New York: Penguin Books, pp. 254–255
  22. ↑ Fisher, Ronald (July 6, 1957), "Dangers Of Cigarette-Smoking", The British Medical Journal , 2 (5035), London: British Medical Association: 43, doi:10.1136/bmj.2.5035.43, JSTOR   25383068, PMC   1961750
  23. ↑ Fisher, Ronald (August 3, 1957), "Dangers Of Cigarette-Smoking", The British Medical Journal , 2 (5039), London: British Medical Association: 297–298, doi:10.1136/bmj.2.5039.297-b, JSTOR   25383439, PMC   1961712
  24. ↑ Fisher, Ronald (1958), "Cigarettes, Cancer, and Statistics" (PDF), The Centennial Review of Arts & Science, 2, East Lansing, Michigan: Michigan State University Press: 151–166, archived (PDF) from the original on 2022-10-09
  25. ↑ Fisher, Ronald (1958), "The Nature of Probability" (PDF), The Centennial Review of Arts & Science, 2, East Lansing, Michigan: Michigan State University Press: 261–274, archived (PDF) from the original on 2022-10-09
  26. ↑ Fisher, Ronald (July 12, 1958), "Lung Cancer and Cigarettes" (PDF), Nature , 182 (4628), London: Nature Publishing Group: 108, Bibcode:1958Natur.182..108F, doi: 10.1038/182108a0 , PMID   13566198, archived (PDF) from the original on 2022-10-09
  27. ↑ Fisher, Ronald (August 30, 1958), "Cancer and Smoking" (PDF), Nature , 182 (4635), London: Nature Publishing Group: 596, Bibcode:1958Natur.182..596F, doi: 10.1038/182596a0 , PMID   13577916, archived (PDF) from the original on 2022-10-09
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