Causality

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.

History of causality
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.

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. 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" 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."

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. 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, and constant conjunction. 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."

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.

Causality in science
Causal inference is the process in which someone can use data to claim there is a causal relationship. 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.

In science, three commonly accepted conditions for establishing a causal relationship are time precedence, relationship, and non-spuriousness. 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.
 * Time precedence &mdash; Time precedence refers to the notion that for something to be considered a cause, the cause must have occurred before the effect. For example, if one suspects that hot temperatures are a cause of sweating, hot temperatures must have been present before the onset of sweating for it to be considered a cause, not after.  Variable X simply being before effect Y is not sufficient for X to be considered a cause of Y and a failure to recognize this fact can lead to the post hoc, ergo propter hoc fallacy.
 * Relationship &mdash; For X and Y to be in a relationship, they must be variables, not constants, and they must have codependency, not independence. In our sweating example, both hot temperatures (variable X) and sweating (effect Y) are variables, and they are codependent as people exposed to hot temperatures (X) should sweat (Y) more often than people exposed to cold temperatures. As temperatures above the mean (X) would be expected to induce sweating above the mean (Y), hot temperature and sweating are said to have positive covariance. If instead variables X and Y have a negative covariance such as cold temperatures and sweating, an entity above the mean for variable X would be expected to be below the mean for variable Y. If X and Y have a covariance of zero, then X is not predictive of Y and vice versa.  Variables with a covariance of zero are independent of one another.
 * Non-spurious &mdash; Even if variable X has both relationship and time-precedence with effect Y, this still does not mean a causal interconnection has been established as the interaction between X and Y must also be non-spurious. For example, if ice cream eating and pool attendance have positive covariance and ice-cream eating occurred before going to the pool, this does not mean that ice cream eating caused one to go to the pool.  Instead both X and Y could be caused by the third variable of hot temperature (Z) and therefore the interconnection between X and Y is spurious and not causal.  Failure to recognize spurious interactions can lead to the cum hoc ergo propter hoc fallacy, a.k.a. correlation does not imply causation.

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.

If there is a causal relationship between variable X and effect Y, then X can be one of a couple different kinds of causes.
 * Necessary causes &mdash; With necessary causes, cause X must be present for effect Y to occur. For example, a naturally-conceived birth (effect Y) in humans necessarily requires a sperm to fertilize an egg (cause X). The presence of cause X, however, does not necessary require effect Y to occur. For example, the female may be on birth control thereby preventing cause X to lead to effect Y.
 * Sufficient causes &mdash; With sufficient causes, the presence of X necessarily implies the occurrence of effect Y, but effect Y does not imply the presence of X. For example, chopping a person's head off with a guillotine (cause X) necessarily implies the occurrence of death (effect Y).  However just because someone died (effect Y) doesn't mean that their head has been chopped off (cause X) as they could also have been killed by hanging, firing squad, electric chair, lethal injection, stoning, or burning at the stake.
 * Contributory causes &mdash; With contributory causes, the presence of cause X makes the presence of Y possible but not with 100% probability. For example, eating refried beans (cause X) could be a cause of flatulence (effect Y), but this effect could also have been due to the soft drink one drank with it.

The Bradford-Hill Criteria
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:
 * 1) Strength of the association &mdash; The stronger the association the more causal the outcome.
 * 2) Consistency of findings &mdash; Finding must apply in different conditions.
 * 3) Specificity of findings &mdash; There must be a one-to-one ratio of cause and effect.
 * 4) Temporal Sequence of association &mdash; The cause must precede the effect.
 * 5) Biological Gradient &mdash; The more exposure, the higher the disease rate.
 * 6) Biological Plausibility &mdash; Is there a biological mechanism for what is happening?
 * 7) Coherence &mdash; Does it match up with what is already known about the disease?
 * 8) Experiment &mdash; Does removal of exposure change the outcome?

Reverse causation
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.

Philosophical concepts related to causality
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.
 * The arrow of time &mdash; Human perception of causality always flows with the direction of time as causes always precede effects. Whether the present moment is physically distinct from the past and future or is merely an emergent property of consciousness has been one of the major unsolved problems in physics. Research into quantum computing, still very much in its preliminary stage, suggests that the arrow of time may be an emergent phenomena. Jayne Thompson, a researcher of quantum computing, had the following to say about causality and time.
 * Determinism versus indeterminism &mdash; Many physicists hold to the belief that quantum mechanics is non-deterministic and for phenomena such as radioactive decay only a probabilistic description of causality can be given. In other words, Aquinas was wrong about causes being clearly demonstrated for every effect. Sometimes events just happen due to chance and therefore are not deterministically caused. The non-deterministic nature of quantum mechanics (Copenhagen interpretation) is the most popular amongst physicists, however, vastly different interpretations of quantum mechanics exist. Einstein famously dissented and controversially promoted a hidden variable theory that would explain why quantum mechanics appeared to be non-deterministic.  Louis de Broglie would also initially dissent in 1927, but he would never fully develop his theory, David Bohm would resurrect it in 1952 giving rise to a deterministic interpretation known as the pilot wave theory. The theory hasn’t gained much support as it doesn’t account for relativity and the motivation behind the initial inception can be seen as dubious.

Causal fallacies (Non causa pro causa)
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. 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. 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. 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. 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. 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. 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.

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. 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. You know, one of the diseases the MMR vaccine is supposed to prevent.

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.