Information AboutCausality |
|
Causality or '''causation''' denotes the relationship between one event (called cause) and another event (called Effect ) which is the consequence (result) of the first. Random House Unabridged Dictionary This informal understanding suffices in everyday usage, however the Philosophical analysis of causality or causation has proved exceedingly 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 most often held to relate Event s, other candidates include Process es, Properties , Variable s, Fact s, 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. According to Sowa (2000), Processes and Causality by John F. Sowa, retrieved Dec. 5, 2006. up until the twentieth century, three assumptions described by Max Born in 1949 were dominant in the definition of causality: #"Causality postulates that there are laws by which the occurrence of an entity B of a certain class depends on the occurrence of an entity A of another class, where the word entity means any physical object, Phenomenon , situation, or event. A is called the cause, B the effect. #"Antecedence postulates that the cause must be prior to, or at least simultaneous with, the effect. #" Contiguity postulates that cause and effect must be in spatial contact or connected by a chain of intermediate things in contact." (Born, 1949, as cited in Sowa, 2000) 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." HISTORY Hindu philosophy The Upanishads (namely Chandogya Upanishad , Sarva Sara Upanishad and Mandukya Upanishad ) and some other texts (namely Brahma Sutras , Yoga Vashishta , Avadhuta Gita and Ashtavakra Gita ) mention causality. However, causality therein is limited to explanations of the creation of the universe. The idea of causality is not itself the subject of study in these scriptures. The ancient scriptures and commentaries on these scriptures have the following common themes with regard to causation:
Western philosophy Aristotle In Metaphysics and Posterior Analytics , Aristotle said: "All causes of things are beginnings; that we have scientific knowledge when we know the cause; that to know a thing's existence 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, determinism, and existentialism 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. LOGIC Necessary and sufficient causes A similar concept occurs in logic, for this see Necessary And Sufficient Conditions Causes are often distinguished into two types: Necessary and sufficient. Necessary causes:
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: # ''If A were a triangle, then A would have three sides.'' # ''If switch S were thrown, then bulb B would light.'' 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. THEORIES Counterfactual theories The philosopher David Lewis notably suggested that all statements about causality can be understood as Counterfactual statements.Lewis, David. (1973) "Causality." ''The Journal of Philosophy'' 70:556-567.Lewis, David. (1979) "Counterfactual Dependence and Time's Arrow" ''Noûs'' 13: 445-476.Lewis, David. (2000) "Causation as Influence" ''The Journal of Philosophy'' 97: 182-197. So, for instance, the statement that John's smoking caused his premature death is equivalent to saying that had John not smoked he would not have prematurely died. (In addition, it need also be true that John did smoke and did prematurely die, although this requirement is not unique to Lewis' theory.) 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.Bunzl, Martin. (1980) "Causal Preemption and Counterfactuals." ''Philosophical Studies'' 37: 115-124Ganeri, Jonardon, Paul Noordhof, and Murali Ramachandran. (1996) "Counterfactuals and Preemptive Causation" ''Analysis'' 56(4): 219-225.Paul, L.A. (1998) "Problems with Late Preemption" ''Analysis'' 58(1): 48-53.). Probabilistic causation 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. Philosophers such as Hugh Mellor Mellor, D.H. (1995) ''The Facts of Causation'', Routledge, ISBN 0-415-19756-2 and Patrick Suppes Suppies, P. (1970) ''A Probabilistic Theory of Causality'', Amsterdam: North-Holland Publishing Company have defined causation in terms of a cause preceding and increasing the probability of the effect. (Additionally, Mellor claims that cause and effect are both facts - not events - since even a non-event, such as the failure of a train to arrive, can cause effects such as my taking the bus. Suppes, by contrast, relies on events defined set-theoretically, and much of his discussion is informed by this terminology.) The establishing of cause and effect, even with this relaxed reading, is notoriously difficult, expressed by the widely accepted statement " Correlation Does Not Imply Causation ". For instance, the observation that smokers have a dramatically increased lung cancer rate does not establish that smoking must be a ''cause'' of that increased cancer rate: maybe there exists a certain genetic defect which both causes cancer and a yearning for nicotine; or even perhaps nicotine craving is a symptom of very early-stage lung cancer which is not otherwise detectable. In Statistics , it is generally accepted that observational studies (like counting cancer cases among smokers and among non-smokers and then comparing the two) can give hints, but can never ''establish'' cause and effect. However, qualitative causal assumptions (e.g., absence of causation between some variables) may permit the establishment of some cause effect relationships from observational studies. The gold standard for causation here is the ''randomized experiment'': take a large number of people, randomly divide them into two groups, force one group to smoke and prohibit the other group from smoking, then determine whether one group develops a significantly higher lung cancer rate. Random assignment plays a crucial role in the inference to causation because, in the long run, it renders the two groups equivalent in terms of all other possible effects on the outcome (cancer) so that any changes in the outcome will reflect only the manipulation (smoking). Obviously, for ethical reasons this Experiment cannot be performed, but the method is widely applicable for less damaging experiments. One limitation of experiments, however, is that whereas they do a good job of testing for the presence of some causal effect they do less well at estimating the size of that effect in a population of interest. (This is a common criticism of studies of safety of food additives that use doses much higher than people consuming the product would actually ingest.) That said, under certain Assumption s, parts of the causal structure among several variables ''can'' be learned from full Covariance or Case Data by the techniques of Path Analysis and more generally, Bayesian Network s. Generally these Inference Algorithm s search through the ''many'' possible causal structures among the Variable s, and remove ones which are strongly incompatible with the observed Correlation s. In general this leaves a set of possible causal relations, which should then be tested by designing appropriate Experiment s. If experimental data is already available, the Algorithm s 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 clearer with a longitudinal epidemiologic study than with a cross-sectional one. Derivation theories The Nobel Prize holder Herbert Simon and Philosopher Nicholas Rescher Simon, Herbert, and Rescher, Nicholas (1966) "Cause and Counterfactual." Philosophy of Science 33: 323–40. claim that the asymmetry of the causal relation is unrelated to the asymmetry of any mode of implication that contraposes. Rather, a causal relation is not a relation between values of variables, but a function of one variable (the cause) on to another (the effect). So, given a system of equations, and a set of variables appearing in these equations, we can introduce an asymmetric relation among individual equations and variables that corresponds perfectly to our commonsense notion of a causal ordering. The system of equations must have certain properties, most importantly, if some values are chosen arbitrarily, the remaining values will be determined uniquely through a path of serial discovery that is perfectly causal. They postulate the inherent serialization of such a system of equations may correctly capture causation in all empirical fields, including physics and economics. Manipulation theories Some theorists have equated causality with manipulability.Collingwood, R.(1940) ''An Essay on Metaphysics.'' Clarendon Press.Gasking, D. (1955) "Causation and Recipes" ''Mind'' (64): 479-487.Menzies, P. and H. Price (1993) "Causation as a Secondary Quality" ''British Journal for the Philosophy of Science'' (44): 187-203.von Wright, G.(1971) ''Explanation and Understanding.'' Cornell University Press. Under these theories, ''x'' causes ''y'' just in case one can change ''x'' in order to change ''y''. This coincides with commonsense notions of causations, since often we ask causal questions in order to change some feature of the world. For instance, we are interested in knowing the causes of crime so that we might find ways of reducing it. 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 account use manipulation as a sign or feature in causation without claiming that manipulation is more fundamental than causation.Pearl, Judea (2000) ''Causality'', Cambridge University Press, ISBN 0-521-77362-8Woodward, James (2003) ''Making Things Happen: A Theory of Causal Explanation''. Oxford University Press, ISBN 0-19-515527-0 Process theories Some theorists are interested in distinguishing between causal processes and non-causal processes (Russell 1948; Salmon 1984).Salmon, W. (1984) ''Scientific Explanation and the Causal Structure of the World''. Princeton University Press.Russell, B. (1948) ''Human Knowledge''. Simon and Schuster. These theorists often want to distinguish between a process and a Pseudo-process . As an example, a ball moving through the air (a process) is contrasted with the motion of a shadow (a pseudo-process). The former is causal in nature while the latter is not. 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. FIELDS Science |
|
|