Cogito ergo sum Logic and rhetoric |
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Causality in its simplest form is the relationship between cause and effect. In science, one's main objective is to find causal relationships, or in simpler terms "This causes that". Most of modern science is based on causal relationships and they are the core pillar of good science. The old mantra "correlation does not imply causation." is often what separates the science from the pseudoscience, the scientists from the cranks, and the evidence-based medicine from the alternative medicine; causality is the glue that holds rational thought together.
One of the first quotes about the concept of causality comes from Plato.
“”Now everything that becomes or is created must of necessity be created by some cause, for without a cause nothing can be created.
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—Timaeus by Plato[1][2] |
Aristotle expanded upon Plato's idea of causality in Physics and Metaphysics where he argued that there were four causes, namely the material, formal, efficient, and final cause.[3] Thomas Aquinas later argued that "from every effect the existence of the cause can be clearly demonstrated, and so we can demonstrate the existence of God from His effects"[4] and the Kalam cosmological argument uses a similar line of reasoning. Both Aquinas's argument and the Kalam cosmological argument expand upon Plato's belief that everything has a cause which is a philosophical position called universal causality. Whether universal causality is true is debatable. The philosopher Wes Morriston offers a detailed counterargument in his paper Must the beginning of the universe have a personal cause? where he argues that "when applied to the beginning of time, the principle that whatever begins to exist must have a cause is not clearly true."[5]
Wes Morriston is hardly the only skeptic when it comes to causality. David Hume was chief among the philosophers to challenge the nature of causality promoted by the Ancient Greeks, Aquinas, and Aquinas's scholastic colleagues. He called into question the very ability of the human mind to understand it.
“”We have sought in vain for an idea of power or necessary connexion in all the sources from which we could suppose it to be derived. It appears that, in single instances of the operation of bodies, we never can, by our utmost scrutiny, discover any thing but one event following another, without being able to comprehend any force or power by which the cause operates, or any connexion between it and its supposed effect… All events seem entirely loose and separate. One event follows another; but we never can observe any tie between them. They seem conjoined, but never connected. And as we can have no idea of any thing which never appeared to our outward sense or inward sentiment, the necessary conclusion seems to be that we have no idea of connexion or power at all, and that these words are absolutely, without any meaning, when employed either in philosophical reasonings or common life.
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—Of the Idea of necessary Connexion Part II by David Hume[6] |
But as skeptical as he was, Hume did not believe causal relationships were beyond rational comprehension. Instead he concluded that humans were able to induce cause-and-effect relationships through careful observation of contiguity, succession,[7] and constant conjunction.[6][8] He would also formulate the problem of induction when he noted that "our reason fails us in the discovery of the ultimate connexion of causes and effects" as it is "impossible for us to satisfy ourselves by our reason, why we shou’d extend that experience beyond those particular instances, which have fallen under our observation."[9]
In more modern times, the discovery that time is relative has had profound implications for the understanding of causality. In physics, the spacetime interval is what defines the flow of causality and reversing its direction causes the direction of causality to be reversed. For things going slower than the speed of light, such a reversal is impossible unless one gets sucked into a black hole.[10]
Causal inference is the process in which someone can use data to claim there is a causal relationship.[8] This is central to most of science, and it is literally science at its core. Some people seem to forget about the part where the data has to support a causal relationship and not just a correlation between the data points. Causal inference is often very important in statistical data, as you are taking a large pre-existing dataset to come to a conclusion, and not a controlled test environment.[11]
In science, three commonly accepted conditions for establishing a causal relationship are time precedence, relationship, and non-spuriousness.[12]
In Multilevel modeling of social problems: A causal perspective, R. B. Smith identifies a stable association between variables and the successful elimination of external factors as being two necessary components for the establishment of linear causality.[13]
Two structural models used with causality are hierarchical and nonhierarchical models. The difference between them is that hierarchical models lack feedback loops whereas nonhierarchical models have them. Feedback loops occur when X causes Y and Y causes X, or when they effect each other through one or more other variables. For example, X causes Y, Y causes Z, and Z causes X. A statistical methodology called path analysis can be done with standardized hierarchical models but the tracing rule of path analysis does not apply for nonhierarchical models.[12]
If there is a causal relationship between variable X and effect Y, then X can be one of a couple different kinds of causes.
In epidemiology, causal relationships can be determined via the Bradford-Hill Criteria. There are 8 parts of this criteria, each one strengthens the possibility of causal relationship between the cause of the disease and the effects of it. The criteria are as follows:
There is often a more a complex relationship between cause and effect than a simple model of X causes Y. This is often seen in medicine when people change their behavior as a result of illness. Heavy drinkers may give up alcohol entirely, smokers with serious health problems may quit, and people with serious heart problems may switch to very healthy diets. As a consequence, studies may give precisely the wrong result to that expected: if lifelong heavy smokers with serious lung problems become ex-smokers, then ex-smokers may seem to be more likely to die of lung problems than people who still smoke (who don't have it quite as bad and are less motivated to quit). This has also been observed with consumption of fatty food, and with alcoholics who quit drinking. For instance, it becomes hard to say if not drinking is worse for you than drinking in moderation because some of the non drinkers are ex-alcoholics. This is particularly a problem in studies on people who already have health conditions or are seriously ill, but it can often be avoided by good experimental design that controls for patient histories and past behaviors as well as present behavior.[16][17][18]
“”The most exciting thing for us is the possible connection with the arrow of time. If causal asymmetry is only found in classical models, it suggests our perception of cause and effect, and thus time, can emerge from enforcing a classical explanation on events in a fundamentally quantum world.[19]
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Given the inherent difficulty of establishing causal relationships, people make causal fallacies all the time. This is particularly true of the social sciences where ethical prohibitions on human experimentation may prevent the elimination of external factors that corrupt the data. Even B. F. Skinner limited his experimentation to animals (despite rumors to the contrary).
The Latin term cum hoc, ergo propter hoc is often used to describe the fallacy that occurs when one incorrectly uses correlation as the basis for causation.[24] The fact that there are at least five different ways A could be correlated with B only one of which is A causes B contributes to the prevalence of this fallacy.[14] If one assumes that A is the cause of B when B is actually the cause of A, then one has committed a reverse causation fallacy.[24][14] If one assumes A is the cause of B due to correlation when both A and B are caused by variable Z, then one has committed a fallacy due to a confounding variable.[25][26][14] If one assumes A is the sole cause of B when B is caused by a complex variety of factors, then one has committed the fallacy of causal oversimplification.[24][25][14] Even if A primarily causes B, the failure to comprehend that B may also cause A can lead to fallacies. For example, an oversimplification of predator-prey relationships would be believing that more predators (A) will cause the numbers of prey (B) to be reduced but failing to recognize that lower numbers of prey (B) also leads to lower numbers of predators (A). Another fallacy is assuming that A is a cause of B when the correlation could simply be due to coincidence.[14][25] This is a fallacy that commonly occurs when people mine data looking for statistical correlations in which case it would be an example of a post-designation fallacy.[27]
The term post hoc, ergo propter hoc is often used to describe the fallacy that occurs when one assumes that because A preceded B, A caused B.[24] This fallacy occurs because some people place too much emphasis on time precedence while pretty much ignoring the other requirements for causality. Andrew Wakefield is a perfect example. Just because vaccinations occur before the discovery of autism in a child doesn't mean vaccines cause autism as you also have to have relationship and non-spuriousness which Wakefield failed to prove. In fact, vaccines have been extensively studied and there is no positive covariance between vaccines and autism. But do you know what does have positive covariance with autism? Rubella infection.[note 1] You know, one of the diseases the MMR vaccine is supposed to prevent.[28]
There are some sources that use some variation of the phrase "fallacy of blaming the victim" to refer to a special case of a fallacy of false cause, where some outside party is much more responsible for the event, and the fallacious argument in question either ignores or conceals this fact.