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The philosophical concept of causality, the principles of causes, or causation, the working of causes, refers to the set of all particular "causal" or "cause-and-effect" relations. A neutral definition is notoriously hard to provide since every aspect of causation has received substantial debate. Most generally, causation is a relationship that holds between events, objects, variables, or states of affairs. It is usually presumed that the cause chronologically precedes the effect. Finally, the existence of a causal relationship generally suggests that - all other things being equal - if the cause occurs the effect will as well (or at least the probability of the effect occurring will increase).
In natural languages, causal relationships can be expressed by the following causative expressions: i) a set of causative verbs [cause, make, create, do, effect, produce, occasion, perform, determine, influence; construct, compose, constitute; provoke, motivate, force, facilitate, induce, get, stimulate; begin, commence, initiate, institute, originate, start; prevent, keep, restrain, preclude, forbid, stop, cease]; ii) a set of causative names [actor, agent, author, creator, designer, former, originator; antecedent, causality, causation, condition, fountain, occasion, origin, power, precedent, reason, source, spring; reason, grounds, motive, need, impulse]; iii) a set of effective names [consequence, creation, development, effect, end, event, fruit, impact, influence, issue, outcome, outgrowth, product, result, upshot]. Causality is the centerpiece of the universe and so the main subject of ontology; for comprehending the nature, meaning, kinds, varieties, and ordering of cause and effect amounts to knowing the beginnings and endings of things, to uncovering the implicit mechanisms of world dynamics, or to having the fundamental scientific knowledge.
- 1 Causation in the history of Western philosophy
- 2 Necessary and sufficient causes
- 3 Causality contrasted with conditionals
- 4 Counterfactual theories of causation
- 5 Probabilistic causation
- 6 Derivation theories
- 7 Manipulation theories
- 8 Process theories
- 9 Causality in psychology
- 10 See also
- 11 External links
- 12 References
Causation in the history of Western philosophy
Aristole, a great mind and ontologist, is the first who saw that 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. Setting the guidelines for all the subsequent causal theories, by specifying its number, nature, principles, elements, varieties, order, and modes of causation, Aristotle's account of the causes of things is the most comprehensive theory up to now. According to Aristotle's theory, all the causes fall into several senses, the total number of which amounts to the ways the question 'why' may be answered; namely, by reference to the matter or the substratum; the essence, the pattern, the form, or the structure; to the primary moving change or the agent and its action; and to the goal, the plan; the end, or the good. Consequently, the major kinds of causes come under the following divisions:
The Material Cause is that from which a thing comes into existence as from its parts, constituents, substratum or materials. This reduces the explanation of causes to the parts (factors, elements, constituents, ingredients) forming the whole (system, structure, compound, complex, composite, or combination) (the part-whole causation).
The Formal Cause tells us what a thing is, that any thing is determined by the definition, form, pattern, essence, whole, synthesis, or archetype. It embraces the account of causes in terms of fundamental principles or general laws, as the whole (macrostructure) is the cause of its parts (the whole-part causation).
The Efficient Cause is that from which the change or the ending of the change first starts. It identifies 'what makes of what is made and what causes change of what is changed' and so suggests all sorts of agents, nonliving or living, acting as the sources of change or movement or rest. Representing the current understanding of causality as the relation of cause and effect, this covers the modern definitions of "cause" as either the agent or agency or particular events or states of affairs.
The Final Cause is that for the sake of which a thing exists or is done, including both purposeful and instrumental actions and activities. The final cause or telos is the purpose or end that something is supposed to serve, or it is that from which and that to which the change is. This also covers modern ideas of mental causation involving such psychological causes as volition, need, motivation, or motives, rational, irrational, ethical, all that gives purpose to behavior.
Additionally, things can be causes of one another, causing each other reciprocally, as hard work causes fitness and vice versa, although not in the same way or function, the one is as the beginning of change, the other as the goal. [Thus Aristotle first suggested a reciprocal or circular causality as a relation of mutual dependence or action or influence of cause and effect.] Also, Aristotle indicated that the same thing can be the cause of contrary effects, its presence and absent may result in different outcomes.
Besides, 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, 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 further investigations of causality will be consisting in imposing a favorite hierarchy on the order (priority) of causes, like 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 instead of explaining the whys and wherefores).
The philosopher who produced the most striking analysis of causality was David Hume. He asserted that it was impossible to know that certain laws of cause and effect always apply - no matter how many times one observes them occurring. Just because the sun has risen every day since the beginning of the Earth does not mean that it will rise again tomorrow. However, it is impossible to go about one's life without assuming such connections and the best that we can do is to maintain an open mind and never presume that we know any laws of causality for certain. This was used as an argument against metaphysics, ideology and attempts to find theories for everything. A.J. Ayer and Karl Popper both claimed that their respective principles of verification and falsifiability fitted Hume's ideas on causality.
- "The reader will find that Spinoza's "cause" is not quite what he is used to. It need not imply temporal succession: indeed, for Spinoza a cause is more the logical ground from which a consequent follows, . . . "For example, it "follows" from the nature of a triangle that its three angles are equal to two right angles. Hence, Spinoza occasionally couples the word "cause" with the term "reason" ("ratio").
- By the phrase efficient cause Spinoza means primarily the cause that produces the effect in question and is quite close to the notion of a sufficient condition. His theory of causality excludes the Aristotelian final cause, the goal or purpose of a thing or event. In his Appendix to Part I Spinoza explicitly claims that final causes are human fictions.
Causality, determinism, and existentialism
The deterministic world-view is one in which the universe is nothing but a chain of events following one after another according to the law of cause and effect. According to incompatibilists holding this worldview there is no such thing as "free will", and therefore, no such thing as morality. 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") 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.
In light of the difficulty philosophers have pointed out in establishing the validity of causal relations, it might seem that the clearest plausible example of causation we have left is our own ability to be the cause of events. If this is so, then our concept of causation would not prevent seeing ourselves as moral agents.
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. If x is a necessary cause of y, then y will only occur if preceded by x. In this case the presence of x does not ensure that y will occur, but the presence of y ensures that x must have occurred. On the other hand, sufficient causes guarantee the effect. So if x is a sufficient cause of y, the presence x guarantees y. However, other events may also cause y, and thus y's presence does not ensure 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 unneccessary 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). So the short circuit is an INUS cause of the house burning down.
Causality contrasted with conditionals
Conditional statements are not statements of causality. Since many different statements may be presented using "If...then..." in English, they are commonly confused; they are distinct, however.
For example all of the following statements are true interpreting "If... then..." as the material conditional:
- If George Bush was president of the United States in 2004, then Germany is in Europe.
- If George Washington was president of the United States in 2004, then Germany is in Europe.
- If George Washington was president of the United States in 2004, then Germany is not in Europe.
The first is true since both the antecedent and the consequent are true. The second and third are both true because the antecedent is false. Of course, none of these statements express a causal connection between the antecedent and consequent.
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
- If Shakespeare didn't write Macbeth then someone else did.
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. Nonetheless, even interpreted counterfactually, the first statement is true. Most sophisticated accounts of causation find some way to deal with this distinction.
Counterfactual theories of causation
The philosopher David Lewis notably suggested that all statements about causality can be understood as counterfactual statements (Lewis 1973, 1979, and 2000). 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 1980; Ganeri, Noordhof, and Ramachandran 1996; Paul 1998)
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 iff 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 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.
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. 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 (ideally in a double-blind setup), 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 assumptions, 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 networks. Generally these inference algorithms 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 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.
However, a worse point for the probability-raising account of causation is that it has some obvious counterexamples. Say Mary and John both want to break a window. Mary is about to throw a rock at it, but when she sees John throw she puts down her rock. John's rock manages to hit the window, and it breaks. However, Mary is a very good shot, and had an 80% chance of hitting and breaking any window she throws a rock at, while John is a bad shot, and only had a 40% chance of hitting and breaking any window he throws a rock at. Thus, although John intuitively caused the window to break, he actually lowered the probability that it would break (from 80% to 40%) by throwing, since he caused Mary to drop her rock rather than throw it.
The Nobel Prize holder Herbert Simon and Philosopher Nicholas Rescher 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) (Simon and Rescher, 1966). 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.
Some theorists have equated causality with manipulability (Collingwood 1940; Gasking 1955; Menzies and Price 1993; von Wright 1971). 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 inituition 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 2000; Woodward 2003).
Some theorists are interested in distinguishing between causal processes and non-causal processes (Russell 1948; Salmon 1984). These theorist 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 second 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.
Causality in psychology
The above theories are attempts to define a reflectively stable notion of causality. This process uses our standard causal intuitions to develop a theory that we would find satisfactory in identifying causes. Another avenue of research is to discover how ordinary causal talk is employed by everyday people without challenging them. This is often studied in psychology.
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.
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.
Symbolism 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". More Info
- Bradford Hill criteria
- Causal analysis
- Causal Markov condition
- Causal reasoning
- Causality loop
- Cherry picking (fallacy)
- Condition of possibility
- Correlation does not imply causation
- Free will
- Linear regression
- Post hoc ergo propter hoc
- Proximate causation
- Risk factors
- Spurious relationship
Stanford Encyclopedia of Philosophy:
- Backwards Causation
- Probabilistic Causation
- Causation and Manipulability
- Counterfactual Theories of Causation
- Causal Processes
- Causation in the Law
- Medieval Theories of Causation
- The Metaphysics of Causation
- Dictionary of the History of Ideas: Causation
- Dictionary of the History of Ideas: Causation in Law
- Dictionary of the History of Ideas: Causation in History
Counterfactual accounts of causation
- Bunzl, Martin. (1980) "Causal Preemption and Counterfactuals." Philosophical Studies 37: 115-124
- Ganeri, Jonardon, Paul Noordhof, and Murali Ramachandran. (1996) "Counterfactuals and Preemptive Causation" Analysis 56(4): 219-225.
- Lewis, David. (1973) "Causality." The Journal of Philosophy 70:556-567.
- ----. (1979) "Counterfactual Dependence and Time's Arrow" Noûs 13: 445-476.
- ----. (2000) "Causation as Influence" The Journal of Philosophy 97: 182-197.
- Paul, L.A. (1998) "Problems with Late Preemption" Analysis 58(1): 48-53.
- Pearl, Judea (2000) Causality, Cambridge University Press, ISBN 0521773628
- Spirtes, Peter, Clark Glymour and Richard Scheines Causation, Prediction, and Search, MIT Press, ISBN 0262194406
- 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.
- Pearl, Judea (2000) Causality. Cambridge University Press, ISBN 0521773628
- Simon, Herbert, and Rescher, Nicholas (1966) "Cause and Counterfactual." Philosophy of Science 33: 323–40.
- von Wright, G.(1971) Explanation and Understanding. Cornell University Press.
- Woodward, James (2003) Making Things Happen: A Theory of Causal Explanation. Oxford University Press, ISBN 0195155270
- Russell, B. (1948) Human Knowledge. Simon and Schuster.
- Salmon, W. (1984) Scientific Explanation and the Causal Structure of the World. Princeton University Press.
- Abdoullaev, A. (2000)The Ultimate of Reality: Reversible Causality, in Proceedings of the 20th World Congress of Philosophy, Boston: Philosophy Documentation Centre, internet site, Paideia Project On-Line: http://www.bu.edu/wcp/MainMeta.htm
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