While this informal understanding will suffice in everyday usage, the philosophical analysis of causality has proven difficult. The work of philosophers to understand causality and how best to characterize it extends over millennia. In the western philosophical tradition explicit discussion stretches back at least as far as Aristotle, and the topic remains a staple in contemporary philosophy journals. Though cause and effect are typically related to events, other candidates include processes, properties, variables, facts, and states of affairs; which of these comprise the correct causal relata, and how best to characterize the nature of the relationship between them, has as yet no universally accepted answer, and remains under discussion.
However, according to Sowa (2000), "relativity and quantum mechanics have forced physicists to abandon these assumptions as exact statements of what happens at the most fundamental levels, but they remain valid at the level of human experience."
In his Posterior Analytics and Metaphysics, Aristotle wrote, "All causes are beginnings..., "... we have scientific knowledge when we know the cause..., and "... to know a thing's nature is to know the reason why it is... This formulation set the guidelines for subsequent causal theories by specifying the number, nature, principles, elements, varieties, order of causes as well as the modes of causation. Aristotle's account of the causes of things is a comprehensive model.
Aristotle's theory enumerates the possible causes which fall into several wide groups, amounting to the ways the question "why" may be answered; namely, by reference to the material worked upon (as by an artisan) or what might be called the substratum; to the essence, i.e., the pattern, the form, or the structure by reference to which the "matter" or "substratum" is to be worked; to the primary moving agent of change or the agent and its action; and to the goal, the plan, the end, or the good that the figurative artisan intended to obtain. As a result, the major kinds of causes come under the following divisions:
Additionally, things can be causes of one another, reciprocally causing each other, as hard work causes fitness, and vice versa - although not in the same way or by means of the same function: the one is as the beginning of change, the other is as its goal. (Thus Aristotle first suggested a reciprocal or circular causality - as a relation of mutual dependence, action, or influence of cause and effect.) Also; Aristotle indicated that the same thing can be the cause of contrary effects - as its presence and absence may result in different outcomes. In speaking thus he formulated what currently is ordinarily termed a "causal factor," e.g., atmospheric pressure as it affects chemical or physical reactions.
Aristotle marked two modes of causation: proper (prior) causation and accidental (chance) causation. All causes, proper and incidental, can be spoken as potential or as actual, particular or generic. The same language refers to the effects of causes; so that generic effects assigned to generic causes, particular effects to particular causes, and operating causes to actual effects. It is also essential that ontological causality does not suggest the temporal relation of before and after - between the cause and the effect; that spontaneity (in nature) and chance (in the sphere of moral actions) are among the causes of effects belonging to the efficient causation, and that no incidental, spontaneous, or chance cause can be prior to a proper, real, or underlying cause per se.
All investigations of causality coming later in history will consist in imposing a favorite hierarchy on the order (priority) of causes; such as "final > efficient > material > formal" (Aquinas), or in restricting all causality to the material and efficient causes or, to the efficient causality (deterministic or chance), or just to regular sequences and correlations of natural phenomena (the natural sciences describing how things happen rather than asking why they happen)..
Causality has taken many journeys in the minds of men for over 3000 years. Determinism and existentialism are but a few of the manifestations of this journey.
The deterministic world-view is one in which the universe is no more than a chain of events following one after another according to the law of cause and effect. To hold this worldview, as an incompatibilist, there is no such thing as "free will". However, compatibilists argue that determinism is compatible with, or even necessary for, free will.
Learning to bear the burden of a meaningless universe, and justify one's own existence, is the first step toward becoming the "Übermensch" (English: "overman" or "superman") that Nietzsche speaks of extensively in his philosophical writings.
Existentialists have suggested that people have the courage to accept that while no meaning has been designed in the universe, we each can provide a meaning for ourselves.
Though philosophers have pointed out the difficulties in establishing theories of the validity of causal relations, there is yet the plausible example of causation afforded daily which is our own ability to be the cause of events. This concept of causation does not prevent seeing ourselves as moral agents.
Theories of causality in Indian philosophy focus mainly on the relationship between cause and effect. The various philosophical schools (darsanas) provide different theories.
The doctrine of satkaryavada affirms that the effect inheres in the cause in some way. The effect is thus either a real or apparent modification of the cause.
The doctrine of asatkaryavada affirms that the effect does not inhere in the cause, but is a new arising.
Among the Buddhist thinkers, Nagarjuna uses a variety of arguments to deny the validity of the cause and effect relationship. More specifically, Nagarjuna denies the existence of any inherently existent cause or inherently existent effect. The causal relationship between conventionally existent causes and effects, however, is not denied.
See Nyaya for some details of the theory of causation in the Nyaya school.
Causes are often distinguished into two types: Necessary and sufficient.
If y is a necessary cause of x, then the presence of x necessarily implies the presence of y. The presence of y, however, does not imply that x will occur.
If x is a sufficient cause of y, then the presence of x necessarily implies the presence of y. However, another cause z may alternatively cause y. Thus the presence of y does not imply the presence of x.
J. L. Mackie argues that usual talk of "cause", in fact, refers to INUS conditions (insufficient and non-redundant parts of unnecessary but sufficient causes). For example; consider the short circuit as a cause of the house burning down. Consider the collection of events, the short circuit, the proximity of flammable material, and the absence of firefighters. Considered together these are unnecessary but sufficient to the house's destruction (since many other collection of events certainly could have destroyed the house). Within this collection; the short circuit is an insufficient but non-redundant part (since the short circuit by itself would not cause the fire, but the fire will not happen without it with everything else being equal). So the short circuit is an INUS cause of the house burning down.
For example all of the following statements are true interpreting "If... then..." as the material conditional:
The first is true since both the antecedent and the consequent are true. The second is true because the antecedent is false and the consequent is true. The third is true because both the consequent and antecedent are both false. These statement are trivial examples. Of course, none of these statements express a causal connection between the antecedent and consequent, but they are true because they do not have the combination of having both true antecedent and false consequent. Logic only requires that truth not be deceptive.
The ordinary indicative conditional seems to have some more structure than the material conditional - for instance, none of the three statements above seem to be correct under an ordinary indicative reading, though the first is closest. But the sentence
seems to be true, even though there is no straightforward causal relation (in this hypothetical situation) between Shakespeare's not writing Macbeth and someone else's actually writing it.
Another sort of conditional, known as the counterfactual conditional has a stronger connection with causality. However, not even all counterfactual statements count as examples of causality. Consider the following two statements:
In the first case it would not be correct to say that A's being a triangle caused it to have three sides, since the relationship between triangularity and three-sidedness is one of definition. It is actually the three sides that determine A's state as a triangle. Nonetheless, even interpreted counterfactually, the first statement is true.
It is probably important to fully grasp the concept of conditionals before the literature on causality can be understood. A crucial stumbling block is that, in everyday usage, conditionals are usually used to describe a general situation. For example "if I drop my coffee, then my shoe gets wet" relates an infinite number of possible events; it is shorthand for "for any fact that would count as 'dropping my coffee', some fact that counts as 'my shoe gets wet' will be true". This general statement will be strictly false if there is any circumstance where I drop my coffee and my shoe doesn't get wet. However, an "if... then..." statement in logic typically relates two specific events or facts - a specific coffee-dropping did or did not occur, and a specific shoe-wetting did or did not follow. Thus, with explicit events in mind, if I drop my coffee and wet my shoe then it is true that "if I dropped my coffee then I wet my shoe", regardless of the fact that yesterday I dropped a coffee in the trash for the opposite effect - the conditional relates to specific facts. More counter-intuitively, if I didn't drop my coffee at all then it is also true that "if I drop my coffee then I wet my shoe", or "dropping my coffee implies I wet my shoe", regardless of whether I wet my shoe or not by any means. This usage would not be counter-intuitive if it weren't for the everyday usage. Briefly, "if X then Y" is equivalent to the first-order logic statement "A implies B" or "not B-and-not-A", where A and B are predicates, but the more familiar usage of an "if A then B" statement would need to be written symbolically using a higher order logic using quantifiers ("for all" and "there exists").
Translating causal into counterfactual statements would only be beneficial if the latter were less problematic than the former. This is indeed the case, as is demonstrated by the structural account of counterfactual conditionals devised by the computer scientist Judea Pearl (2000). This account provides clear semantics and effective algorithms for computing counterfactuals which, in contrast to Lewis' closest world semantics does not rely on the ambiguous notion of similarity among worlds. For instance, one can compute unambiguously the probability that John would be alive had he not smoked given that, in reality, John did smoke and did die. The quest for a counterfactual interpretation of causal statements is therefore justified.
One problem Lewis' theory confronts is causal preemption. Suppose that John did smoke and did in fact die as a result of that smoking. However, there was a murderer who was bent on killing John, and would have killed him a second later had he not first died from smoking. Here we still want to say that smoking caused John's death. This presents a problem for Lewis' theory since, had John not smoked, he still would have died prematurely. Lewis himself discusses this example, and it has received substantial discussion (cf.). A structural solution to this problem has been given in [Halpern and Pearl, 2005].
Interpreting causation as a deterministic relation means that if A causes B, then A must always be followed by B. In this sense, war does not cause deaths, nor does smoking cause cancer. As a result, many turn to a notion of probabilistic causation. Informally, A probabilistically causes B if A's occurrence increases the probability of B. This is sometimes interpreted to reflect imperfect knowledge of a deterministic system but other times interpreted to mean that the causal system under study has an inherently chancy nature.
The theory of "causal calculus" permits one to infer interventional probabilities from conditional probabilities in causal Bayesian Networks with unmeasured variables. One very practical result of this theory is the characterization of confounding variables, namely, a sufficient set of variables that, if adjusted for, would yield the correct causal effect between variables of interest. It can be shown that a sufficient set for estimating the causal effect of on is any set of non-descendants of that -separate from after removing all arrows emanating from . This criterion, called "backdoor", provides a mathematical definition of "confounding" and helps researchers identify accessible sets of variables worthy of measurement.
Alternative methods of structure learning search through the many possible causal structures among the variables, and remove ones which are strongly incompatible with the observed correlations. In general this leaves a set of possible causal relations, which should then be tested by designing appropriate experiments. If experimental data is already available, the algorithms can take advantage of that as well. In contrast with Bayesian Networks, path analysis and its generalization, structural equation modeling, serve better to estimate a known causal effect or test a causal model than to generate causal hypotheses.
For nonexperimental data, causal direction can be hinted if information about time is available. This is because (according to many, though not all, theories) causes must precede their effects temporally. This can be set up by simple linear regression models, for instance, with an analysis of covariance in which baseline and follow up values are known for a theorized cause and effect. The addition of time as a variable, though not proving causality, is a big help in supporting a pre-existing theory of causal direction. For instance, our degree of confidence in the direction and nature of causality is much greater when supported by data from a longitudinal study than by data from a cross-sectional study.
These theories have been criticized on two primary grounds. First, theorists complain that these accounts are circular. Attempting to reduce causal claims to manipulation requires that manipulation is more basic than causal interaction. But describing manipulations in non-causal terms has provided a substantial difficulty.
The second criticism centers around concerns of anthropocentrism. It seems to many people that causality is some existing relationship in the world that we can harness for our desires. If causality is identified with our manipulation, then this intuition is lost. In this sense, it makes humans overly central to interactions in the world.
Some attempts to save manipulability theories are recent accounts that don't claim to reduce causality to manipulation. These accounts use manipulation as a sign or feature in causation without claiming that manipulation is more fundamental than causation. -->
Salmon (1984) claims that causal processes can be identified by their ability to transmit an alteration over space and time. An alteration of the ball (a mark by a pen, perhaps) is carried with it as the ball goes through the air. On the other hand an alteration of the shadow (insofar as it is possible) will not be transmitted by the shadow as it moves along.
These theorists claim that the important concept for understanding causality is not causal relationships or causal interactions, but rather identifying causal processes. The former notions can then be defined in terms of causal processes.
In addition, many scientists in a variety of fields disagree that experiments are necessary to determine causality. For example, the link between smoking and lung cancer is considered proven by health agencies of the United States government, but experimental methods (for example, randomized controlled trials) were not used to establish that link. This view has been controversial. In addition, many philosophers are beginning to turn to more relativized notions of causality . Rather than providing a theory of causality in toto , they opt to provide a theory of causality in biology or causality in physics .
The treatment of the concept of causality within the Second Law of Thermodynamics yields a loss in the translation. The statistical basis of the maintenance of the exchange of entropy confines the subject to an extent such that the observer loses perspective. The 2nd Law states that "in a closed system, entropy cannot decrease". This is a corollary of the concept that an effect cannot be greater than the cause.
Attribution theory is the theory concerning how people explain individual occurrences of causation. Attribution can be external (assigning causality to an outside agent or force - claiming that some outside thing motivated the event) or internal (assigning causality to factors within the person - taking personal responsibility or accountability for one's actions and claiming that the person was directly responsible for the event). Taking causation one step further, the type of attribution a person provides influences their future behavior.
Whereas David Hume argued that causes are inferred from non-causal observations, Immanuel Kant claimed that people have innate assumptions about causes. Within psychology, Patricia Cheng (1997) attempted to reconcile the Humean and Kantian views. According to her power PC theory, people filter observations of events through a basic belief that causes have the power to generate (or prevent) their effects, thereby inferring specific cause-effect relations. The theory assumes probabilistic causation. Pearl (2000) has shown that Cheng's causal power can be given a counterfactual interpretation, (i.e., the probability that, absent and , would be true if were true) and is computable therefore using structural models.
Causation and salience
Our view of causation depends on what we consider to be the relevant events. Another way to view the statement, "Lightning causes thunder" is to see both lightning and thunder as two perceptions of the same event, viz., an electric discharge that we perceive first visually and then aurally.
Naming and causality
While the names we give objects often refer to their appearance, they can also refer to an object's causal powers - what that object can do, the effects it has on other objects or people. David Sobel and Alison Gopnik from the Psychology Department of UC Berkeley designed a device known as the blicket detector which suggests that "when causal property and perceptual features are equally evident, children are equally as likely to use causal powers as they are to use perceptual properties when naming objects". According to Jacques Lacan (seminar X, "L'Angoisse", 1962-63, ch. XVI), the cause is the shadow of the blind spot in our knowledge. Its secret must be searched in anxiety (angoisse), and in the function of the object. Every time we consider something that is brought into the field of knowledge, desire is present, and the function of the cause makes its appearance. Desire is always to desire the body, the body of the other. The cause is related to the body. Lacan stresses the importance of the Bible, the Jewish Bible, because it is there very clearly, we pay the debt with our body, with parts of the body. Lacan says ti semitic feelings may be based in the fact that Jewish tradition forces us to see the importance of the debt and its relation with the body.Shylock is the presence of this structure: to pay with our flesh. And the function of the cause is in direct relationship with it.
One of the classic arguments for the existence of God is known as the "Cosmological argument" or "First cause" argument. It works from the premise that every natural event is the effect of a cause. If this is so, then the events that caused today's events must have had causes themselves, which must have had causes, and so forth. If the chain never ends, then one must uphold the hypothesis of an "actual infinite", which is often regarded as problematic, see Hilbert's paradox of the Grand Hotel. If the chain does end, it must end with a non-natural or supernatural cause at the start of the natural world -- e.g. a creation by God.
As F.R. John Laux, M.A. puts it,
"In our experience every event (effect) is determined by a cause. That cause is in its turn determined by another cause. But we cannot assume an infinite series of causes, because an infinite series with no beginning involves a contradiction. And even if we did suppose the possibility of an infinite series, that would not explain how causation began. Hence there must be an uncaused Cause, the ultimate Cause of all the events which proceed from it. This ultimate and supreme Cause we call God.
Two questions that can help to focus the argument are:
Critics of this argument point out problems with it. The Big Bang theory states that it is the point in which all dimensions came into existence, the start of both space and time. Then, the question "What was there before the Universe?" makes no sense; the concept of "before" becomes meaningless when considering a situation without time, and thus the concepts of cause and effect so necessary to the cosmological argument no longer apply. This has been put forward by Stephen Hawking, who said that asking what occurred before the Big Bang is like asking what is north of the North Pole. However, some cosmologists and physicists do attempt to investigate what could have occurred before the Big Bang, using such scenarios as the collision of branes to give a cause for the Big Bang.
A question related to this argument is which came first, the chicken or the egg?
For example, if a person always does good deeds then it is believed that he or she will be "rewarded" for his or her behavior with fortunate events such as avoiding fatal accident or winning the lottery. If he or she always commits antagonistic behaviors, then it is believed that he will be punished with unfortunate events.
According to Buddhism, inequality amongst living beings is due not only to heredity, environment, "nature and nurture", but also to Karma. In other words, it is the result of our own past actions and our own present doings. We ourselves are responsible for our own happiness and misery. We create our own Heaven. We create our own Hell. We are the architects of our own fate.
Perplexed by the seemingly inexplicable, apparent disparity that existed among humanity, a young truth-seeker approached the Buddha and questioned him regarding this intricate problem of inequality:
"What is the cause, what is the reason, O Lord," questioned he, "that we find amongst mankind the short-lived and long-lived, the healthy and the diseased, the ugly and beautiful, those lacking influence and the powerful, the poor and the rich, the low-born and the high-born, and the ignorant and the wise?"
The Buddha’s reply was:
"All living beings have actions (Karma) as their own, their inheritance, their congenital cause, their kinsman, their refuge. It is Karma that differentiates beings into low and high states."
He then explained the cause of such differences in accordance with the law of cause and effect.
Destiny might be considered reverse causality in that a cause is predated by an effect; e.g., "I found a twenty dollar bill on the ground because later I would need it."
Some modern religious movements have postulated along the lines of philosophical idealism that causality is actually reversed from the direction normally presumed, and that causality does not proceed inward, from external random causes toward effects on a perceiving individual, but rather outward, from a perceiving individual's causative mental requests toward responsive external physical effects that only seem to be independent causes. Such thought gives rise to new causality principles such as the doctrine of responsibility assumption.
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