Causality is the relationship between an event (the ''cause'') and a second event (the ''effect''), where the second event is understood as a consequence of the first.

Though the causes and effects are typically related to changes or event, candidates include objects, processes, properties, variables, facts, and states of affairs; characterizing the causal relationship can be the subject of much debate.

The philosophical treatment of causality extends over millennia. In the Western philosophical tradition, discussion stretches back at least to Aristotle, and the topic remains a staple in contemporary philosophy.

History

Western philosophy

Aristotle

Aristotle distinguished between four causes, or four explanations, that each answer the question "why?" in different ways. These various means of explanation can be divided into four general types as follows:

The material cause is the physical matter, the mass of "raw material" of which something is "made" (of which it consists).

The formal cause tells us what, by analogy to the plans of an artisan, a thing is intended and planned to be.

The efficient cause is that external entity from which the change or the ending of the change first starts.

The final cause is that for the sake of which a thing exists, or is done - including both purposeful and instrumental actions. The final cause, or telos, is the purpose, or end, that something is supposed to serve.

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 accidental, 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" (Thomas 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).

After the Middle Ages

With the end of the Middle Ages however, Aristotle's approach, especially concerning formal and final causes, was criticized by authors such as Niccolò Machiavelli, in the field of political thinking, and Francis Bacon, concerning science more generally. A widely used modern definition of causality was originally given by David Hume. He denied that we can ever perceive cause and effect, except by developing a habit or custom of mind where we come to associate two types of object or event, always contiguous and occurring one after the other. In Part III, section XV, Hume expanded this to a list of eight ways of judging whether two things might be cause and effect. The first three: :1. "The cause and effect must be contiguous in space and time." :2. "The cause must be prior to the effect." :3. "There must be a constant union betwixt the cause and effect. 'Tis chiefly this quality, that constitutes the relation." And then additionally there are three connected criteria which come from our experience and which are "the source of most of our philosophical reasonings": :4. "The same cause always produces the same effect, and the same effect never arises but from the same cause. This principle we derive from experience, and is the source of most of our philosophical reasonings." :5. Hanging upon the above, Hume says that "where several different objects produce the same effect, it must be by means of some quality, which we discover to be common amongst them." :6. And "founded on the same reason": "The difference in the effects of two resembling objects must proceed from that particular, in which they differ." And then two more: :7. "When any object encreases or diminishes with the encrease or diminution of its cause, 'tis to be regarded as a compounded effect, deriv'd from the union of the several different effects, which arise from the several different parts of the cause." :8. An "object, which exists for any time in its full perfection without any effect, is not the sole cause of that effect, but requires to be assisted by some other principle, which may forward its influence and operation."

However, according to Sowa (2000), citing Max Born in 1949, "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."

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.

Existentialists have suggested that people believe 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.

Indian philosophy

Karma is the belief held by some major religions that a person's actions cause certain effects in the current life and/or in future life, positively or negatively. The various philosophical schools (darsanas) provide different accounts of the subject. 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. See Nyaya for some details of the theory of causation in the Nyaya school.

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. A third type of causation, which requires neither necessity nor sufficiency in and of itself, but which contributes to the effect, is called a "contributory cause."

Necessary causes:

If ''x'' is a necessary cause of ''y'', then the presence of ''y'' necessarily implies the presence of ''x''. The presence of ''x'', however, does not imply that ''y'' will occur.

Sufficient causes:

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''.

Contributory causes:

A cause may be classified as a "contributory cause," if the presumed cause precedes the effect, and altering the cause alters the effect. It does not require that all those subjects which possess the contributory cause experience the effect. It does not require that all those subjects which are free of the contributory cause be free of the effect. In other words, a contributory cause may be neither necessary nor sufficient but it must be contributory.

J. L. Mackie argues that usual talk of "cause," in fact refers to INUS conditions (insufficient but non-redundant parts of a condition which is itself unnecessary but sufficient for the occurrence of the effect). For example, a short circuit as a cause for a house burning down. Consider the collection of events: the short circuit, the proximity of flammable material, and the absence of firefighters. Together these are unnecessary but sufficient to the house's burning down (since many other collections of events certainly could have led to the house burning down, for example shooting the house with a flamethrower in the presence of oxygen etc. etc.). Within this collection, the short circuit is an insufficient (since the short circuit by itself would not have caused the fire, but the fire would not have happened without it, everything else being equal) but non-redundant part of a condition which is itself unnecessary (since something else could have also caused the house to burn down) but sufficient for the occurrence of the effect . So, the short circuit is an INUS condition for the occurrence of the house burning down.

Causality contrasted with conditionals

Conditional statements are ''not'' statements of causality. An important distinction is that statements of causality require the antecedent to precede or coincide with the consequent in time, whereas conditional statements do not require this temporal order. Confusion commonly arises since many different statements in English may be presented using "If ..., then ..." form (and, arguably, because this form is far more commonly used to make a statement of causality). The two types of statements are distinct, however.

For example, all of the following statements are true when interpreting "If ..., then ..." as the material conditional: # ''If Barack Obama is president of the United States in 2011, then Germany is in Europe.'' # ''If George Washington is president of the United States in 2011, then .''

The first is true since both the antecedent and the consequent are true. The second is true, regardless of the consequent statement that follows, because the antecedent is false.

The ordinary indicative conditional has somewhat more structure than the material conditional. For instance, although the first is the closest, neither of the preceding two statements seems true as an ordinary indicative reading. But the sentence

''If Shakespeare of Stratford-on-Avon did not write Macbeth, then someone else did.''

intuitively 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, the counterfactual conditional, has a stronger connection with causality, yet even counterfactual statements are not all 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 that of definition. The property of having three sides actually determines A's state as a triangle. Nonetheless, even when interpreted counterfactually, the first statement is true.

A full grasp of the concept of conditionals is important to understanding the literature on causality. A crucial stumbling block is that conditionals in everyday English are usually loosely 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 counterintuitively, 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 counterintuitive if it were not for the everyday usage. Briefly, "If X then Y" is equivalent to the first-order logic statement "A implies B" or "not A-and-not-B", 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").

Questionable Cause

Fallacies of questionable cause, also known as causal fallacies, non causa pro causa ("non-cause for cause" in Latin) or false cause, are informal fallacies where a cause is incorrectly identified.

Theories

Counterfactual theories

A counterfactual conditional, subjunctive conditional, or remote conditional, abbreviated cf, is a conditional (or "if-then") statement indicating what would be the case if its antecedent were true. This is to be contrasted with an indicative conditional, which indicates what is (in fact) the case if its antecedent is (in fact) true.

Psychological research shows that people's thoughts about the causal relationships between events influences their judgments of the plausibility of counterfactual alternatives, and conversely, their counterfactual thinking about how a situation could have turned out differently changes their judgements of the causal role of events and agents. Nonetheless, their identification of the cause of an event, and their counterfactual thought about how the event could have turned out differently do not always coincide. People distinguish between various different sorts of causes, e.g., strong and weak causes. Research in the psychology of reasoning shows that people make different sorts of inferences from different sorts of causes.

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 is inherently probabilistic, such as quantum mechanics.

Causal Calculus

When experiments are infeasible or illegal, the derivation of cause effect relationship from observational studies must rest on some qualitative theoretical assumptions, for example, that symptoms do not cause diseases, usually expressed in the form of missing arrows in causal graphs such as Bayesian Networks or path diagrams. The mathematical theory underlying these derivations relies on the distinction between ''conditional probabilities'', as in $P(cancer|smoking)$, and ''interventional probabilities'', as in $P(cancer|do(smoking))$. The former reads: "the probability of finding cancer in a person known to smoke" while the latter reads: "the probability of finding cancer in a person ''forced'' to smoke". The former is a statistical notion that can be estimated directly in observational studies, while the latter is a causal notion (also called "causal effect") which is what we estimate in a controlled randomized experiment.

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 $X$ on $Y$ is any set of non-descendants of $X$ that $d$-separate $X$ from $Y$ after removing all arrows emanating from $X$. This criterion, called "backdoor", provides a mathematical definition of "confounding" and helps researchers identify accessible sets of variables worthy of measurement.

Structure Learning

While derivations in Causal Calculus rely on the structure of the causal graph, parts of the causal structure can, under certain assumptions, be learned from statistical data. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl (1987) and rests on the distinction between the three possible types of causal substructures allowed in a directed acyclic graph (DAG):

1. $X\; \backslash rightarrow\; Y\; \backslash rightarrow\; Z$
2. $X\; \backslash leftarrow\; Y\; \backslash rightarrow\; Z$
3. $X\; \backslash rightarrow\; Y\; \backslash leftarrow\; Z$

Type 1 and type 2 represent the same statistical dependencies (i.e., $X$ and $Z$ are independent given $Y$) and are, therefore, indistinguishable. Type 3, however, can be uniquely identified, since $X$ and $Z$ are marginally independent and all other pairs are dependent. Thus, while the ''skeletons'' (the graphs stripped of arrows) of these three triplets are identical, the directionality of the arrows is partially identifiable. The same distinction applies when $X$ and $Z$ have common ancestors, except that one must first condition on those ancestors. Algorithms have been developed to systematically determine the skeleton of the underlying graph and, then, orient all arrows whose directionality is dictated by the conditional independencies observed.

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.

Derivation theories

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). 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. Under these theories, ''x'' causes ''y'' only in the case that 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 accounts use manipulation as a sign or feature in causation without claiming that manipulation is more fundamental than causation.

Process theories

Some theorists are interested in distinguishing between causal processes and non-causal processes (Russell 1948; Salmon 1984). 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

Causality is a basic assumption of science. Within the scientific method, scientists set up experiments to determine causality in the physical world. Imbedded within the scientific method and experiments is a hypothesis or several hypotheses about causal relationships. The scientific method is used to test the hypotheses.

Physics

Physicists conclude that certain elemental forces: gravity, the strong and weak nuclear forces, and electromagnetism are the four fundamental forces that cause all other events in the universe. The notion of causality that appears in many different physical theories is hard to interpret in ordinary language. One problem is typified by earth's interaction with the moon. It is inaccurate to say, "the moon exerts a gravitic pull and then the tides rise." In Newtonian mechanics gravity, rather, is a constant observable relationship among masses, and the movement of the tides is an example of that relationship. There are no discrete events or "pulls" that can be said to precede the rising of tides. Interpreting gravity causally is even more complicated in general relativity. Similarly, quantum mechanics is another branch of physics in which the concept of causality is challenged by paradoxes. For statistical generalization, causality has further implications due to its intimate connection with the Second Law of Thermodynamics (see the fluctuation theorem).

Engineering

A causal system is a system with output and internal states that depends only on the current and previous input values. A system that has ''some'' dependence on input values from the future (in addition to possible past or current input values) is termed an acausal system, and a system that depends ''solely'' on future input values is an anticausal system. Acausal filters, for example, can only exist as postprocessing filters, because these filters can extract future values from a memory buffer or a file.

Biology and medicine

Austin Bradford Hill built upon the work of Hume and Popper and suggested in his paper "The Environment and Disease: Association or Causation?" that aspects of an association such as strength, consistency, specificity and temporality be considered in attempting to distinguish causal from noncausal associations in the epidemiological situation. See Bradford-Hill criteria.

Psychology

Psychologists take an empirical approach to causality, investigating how people and non-human animals detect or infer causation from sensory information, prior experience and innate knowledge.

Attribution :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.

The intention behind the cause or the effect can be covered by the subject of action (philosophy). See also accident; blame; intent; and responsibility.

Causal powers :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 $x$ and $y$, $y$ would be true if $x$ were true) and is computable therefore using structural models. Within a Bayesian framework, the power PC theory can be interpreted as a noisy-OR function used to compute likelihoods (Griffiths & Tenenbaum, 2005)

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".

Perception of Launching Events :Some researchers such as Anjan Chatterjee at the University of Pennsylvania and Jonathan Fugelsang at the University of Waterloo are using neuroscience techniques to investigate the neural and psychological underpinnings of causal launching events in which one object causes another object to move. Both temporal and spatial factors can be manipulated.

Economics

Economics usually employs pre-existing data rather than experimental data to infer causality. The body of statistical techniques that are used in economics is referred to as econometrics, and involves substantial use of regression analysis. Typically a linear relationship such as

:$y\_i\; =\; a\_0\; +\; a\_1x\_\{1,i\}\; +\; a\_2x\_\{2,i\}\; +\; ...\; +\; a\_kx\_\{k,i\}\; +\; e\_i$

is postulated, in which $y\_i$ is the ''i''^th observation of the dependent variable (hypothesized to be the caused variable), $x\_\{j,i\}$ for ''j''=1,...,''k'' is the ''i''^th observation on the ''j''^th independent variable (hypothesized to be a causative variable), and $e\_i$ is the error term for the ''i''^th observation (containing the combined effects of all other causative variables, which must be uncorrelated with the included independent variables). If there is reason to believe that none of the $x\_j$s is caused by ''y'', then estimates of the coefficients $a\_j$ are obtained. If the null hypothesis that $a\_j=0$ is rejected, then the alternative hypothesis that $a\_\{j\}\; \backslash ne\; 0$ and equivalently that $x\_j$ causes ''y'' cannot be rejected. On the other hand, if the null hypothesis that $a\_j=0$ cannot be rejected, then equivalently the hypothesis of no causal effect of $x\_j$ on ''y'' cannot be rejected. Here the notion of causality is one of contributory causality as discussed above: If the true value $a\_j\; \backslash ne\; 0$, then a change in $x\_j$ will result in a change in ''y'' ''unless'' some other causative variable(s), either included in the regression or implicit in the error term, change in such a way as to exactly offset its effect; thus a change in $x\_j$ is ''not sufficient'' to change ''y''. Likewise, a change in $x\_j$ is ''not necessary'' to change ''y'', because a change in ''y'' could be caused by something implicit in the error term (or by some other causative explanatory variable included in the model).

The above way of testing for causality requires belief that there is no reverse causation, in which ''y'' would cause $x\_j$. This belief can be established in one of several ways. First, the variable $x\_j$ may be a non-economic variable: for example, if rainfall amount $x\_j$ is hypothesized to affect the futures price ''y'' of some agricultural commodity, it is impossible that in fact the futures price affects rainfall amount (provided that cloud seeding is never attempted). Second, the instrumental variables technique may be employed to remove any reverse causation by introducing a role for other variables (instruments) that are known to be unaffected by the dependent variable. Third, the principle that effects cannot precede causes can be invoked, by including on the right side of the regression only variables that precede in time the dependent variable; this principle is invoked, for example, in testing for Granger causality and in its multivariate analog, vector autoregression, both of which control for lagged values of the dependent variable while testing for causal effects of lagged independent variables.

Regression analysis controls for other relevant variables by including them as regressors (explanatory variables). This helps to avoid false inferences of causality due to the presence of a third, underlying, variable that influences both the potentially causative variable and the potentially caused variable: its affect on the potentially caused variable is captured by directly including it in the regression, so that effect will not be picked up as an indirect effect through the potentially causative variable of interest.

Management

For quality control in manufacturing in the 1960s, Kaoru Ishikawa developed a cause and effect diagram, known as an Ishikawa diagram or fishbone diagram. The diagram categorizes causes, such as into the six main categories shown here. These categories are then sub-divided. Ishikawa's method identifies "causes" in brainstorming sessions conducted among various groups involved in the manufacturing process. These groups can then be labeled as categories in the diagrams. The use of these diagrams has now spread beyond quality control, and they are used in other areas of management and in design and engineering. Ishikawa diagrams have been criticized for failing to make the distinction between necessary conditions and sufficient conditions. It seems that Ishikawa was not even aware of this distinction.

Humanities

History

In the discussion of history, events are often considered as if in some way being agents that can then bring about other historical events. Thus, the combination of poor harvests, the hardships of the peasants, high taxes, lack of representation of the people, and kingly ineptitude are among the ''causes'' of the French Revolution. This is a somewhat Platonic and Hegelian view that reifies causes as ontological entities. In Aristotelian terminology, this use approximates to the case of the ''efficient'' cause.

Law

According to law and jurisprudence, legal cause must be demonstrated to hold a defendant liable for a crime or a tort (i.e. a civil wrong such as negligence or trespass). It must be proven that causality, or a "sufficient causal link" relates the defendant's actions to the criminal event or damage in question. Causation is also an essential legal element that must be proven to qualify for remedy measures under international trade law.

Theology

Note the concept of omnicausality in theology and in philosophy.

See also

Statistics:

Causal loop diagram

Causal Markov condition

Correlation does not imply causation

Experimental design

Granger causality

Linear regression

Randomness

Rubin Causal Model

Validity (statistics)

Physics:

Anthropic principle

Arrow of time

Butterfly effect

Chain reaction

Grandfather paradox

Quantum Zeno effect

Retrocausality

Schrödinger's cat

Wheeler-Feynman absorber theory

Philosophy:

Aetiology

Chance (philosophy)

Chicken or the egg

Condition of possibility

Determinism

Mill's Methods

Newcomb's paradox

Ontological paradox

Post hoc ergo propter hoc

Predestination paradox

Proximate and ultimate causation

General

Cosmological argument

Domino effect

Sequence of events

Mathematics:

Causal filter

Causal system

Causality conditions

Chaos theory

Psychology & Medicine:

Adverse effect

Clinical trial

Force Dynamics

Iatrogenesis

Nocebo

Placebo

Scientific control

Synchronicity

Suggestibility

Suggestion

Sociology & Economics:

Instrumental variable

Root cause analysis

Self-fulfilling prophecy

Unintended consequence

Virtuous circle and vicious circle

References

Other references

Azamat Abdoullaev (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

Green, Celia (2003). ''The Lost Cause: Causation and the Mind-Body Problem''. Oxford: Oxford Forum. ISBN 0-9536772-1-4 Includes three chapters on causality at the microlevel in physics.

Judea Pearl (2000). ''Causality: Models of Reasoning and Inference'' Cambridge University Press ISBN 978-0521773621

Rosenberg, M. (1968). ''The Logic of Survey Analysis''. New York: Basic Books, Inc.

Spirtes, Peter, Clark Glymour and Richard Scheines ''Causation, Prediction, and Search'', MIT Press, ISBN 0-262-19440-6

University of California journal articles, including Judea Pearl's articles between 1984-1998 .

External links

"The Art and Science of Cause and Effect": a slide show and tutorial lecture by Judea Pearl

Donald Davidson: Causal Explanation of Action The Internet Encyclopedia of Philosophy

leadsto: a public available collection comprising more than 5000 causalities

Causal inference in statistics: An overview, by Judea Pearl (September 2009)

Stanford Encyclopedia of Philosophy

Backwards Causation

Causal Processes

Causation and Manipulability

Causation in the Law

Counterfactual Theories of Causation

Medieval Theories of Causation

Probabilistic Causation

The Metaphysics of Causation

General

''Dictionary of the History of Ideas'': Causation

''Dictionary of the History of Ideas'': Causation in History

''Dictionary of the History of Ideas'': Causation in Law

Category:Conditionals Category:Philosophy of science Category:Fundamental physics concepts Category:Concepts in epistemology Category:Concepts in metaphysics

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Coordinates	16°40′0″N96°11′0″N
name	Charles Tilly
birth date	1929
birth place	Lombard, United States
death date	2008 (age 78)
nationality	American
fields	Social Science Sociology Political Science History
workplaces	University of Delaware Harvard University University of Toronto University of Michigan The New School Columbia University Institute for Social and Economic Research and Policy
alma mater	Harvard University (Ph.D.) University of Oxford
footnotes	}}

Charles Tilly (May 27, 1929 – April 29, 2008) was an American sociologist, political scientist, and historian who wrote on the relationship between politics and society. He was the Joseph L. Buttenwieser Professor of Social Science at Columbia University.

Personal life and education

Tilly was born on May 20, 1929, in Lombard, Illinois (near Chicago). He graduated from Harvard University with a Bachelor of Arts magna cum laude in 1950 and completed Doctor of Philosophy in Sociology in 1958.

Charles Tilly died on April 29, 2008 from lymphoma. As he was fading in the hospital, he got one characteristic sentence out to early student Barry Wellman: "It's a complex situation." In his obituary, Columbia University president Lee C. Bollinger stated that Tilly "literally wrote the book on the contentious dynamics and the ethnographic foundations of political history".

Academic career

Charles Tilly taught at the University of Delaware, Harvard University, the University of Toronto, the University of Michigan, The New School, and Columbia University. At Columbia, he was the Joseph L. Buttenwieser Professor of Social Science. Over the course of his career, Tilly wrote more than 600 articles and 51 books and monographs..

Academic work

Tilly's academic work covered multiple topics in the social sciences and influenced scholarship in disciplines outside of sociology, including history and political science. He is considered a major figure in the development of historical sociology, the early use of quantitative methods in historical analysis, the methodology of event cataloguing, the turn towards relational and social-network modes of inquiry, the development of process- and mechanism-based analysis, as well as the study of: contentious politics, social movements, the history of labor, state formation, revolutions, democratization, inequality, and urban sociology.

State Formation

Examining political, social, and technological change in Europe from the Middle Ages to the present, Tilly attempted to explain the unprecedented success of the nation-state as the dominant polity on Earth. According to his theory, military innovation in pre-modern Europe (especially gunpowder and mass armies) made war extremely expensive. As a result, only states with a sufficient amount of capital and a large population could afford paying for their security and ultimately survive in the hostile environment. Institutions of the modern state (such as taxes) were created to allow war-making.

Contentious Politics

In opposition to individualistic, dispositional analyses of contentious politics, Tilly's work emphasizes how dynamics of social protest are tied to their political, social and economic context. Where previous studies of collective violence had argued their atypical nature, Tilly amassed a battery of evidence to show that they typically arise from the organization of normally non-violent political contentions.

Tilly's work has had considerable influence on the study of social movements. Ironically, Charles Tilly was initially reluctant to tackle social movements. “I avoided writing about social movements for about twenty years because I felt that the term had become swollen and imprecise,” he said in a 2007 interview. “The phenomenon of the social movement looked to me like a historically specific form of politics parallel to the electoral campaign and the collective seizure of food, not a universal category of human action.”

The notion of locating the birth of the social movement in a specific time and place occurred to Tilly while he was writing his pathbreaking work on the transformation of British popular politics, ''Popular Contention in Great Britain, 1758-1834''. In the process of collating and analyzing the 8,088 contentious gatherings (CGs) that form the basis of the book, Tilly noticed that one momentous change in how ordinary people made collective claims on public authorities turned out to be the invention of what we now call the social movement. “I couldn’t help seeing that in Britain, at least, social movements didn’t exist in the mid-18th century but had become a dominant form of popular politics by the 1830s. That started me writing about the history of social movements, first in Western Europe, then finally across the world as a whole.”

Instead of defining all forms of social protest as social movements, Tilly uses a narrower definition. Conceptually, he argues that social movements share some elements with other forms of political contention such as coups, electoral campaigns, strikes, revolutions, and interest-group politics, but have their own distinct characteristics. He argues that the social movement developed in the West after 1750 and spread throughout the world through colonialism, trade and migration. Local populations are more likely to experiment with the social movement with democratization, and when successful, are more likely to incorporate it into their political struggles.

Tilly argues that social movements combine:

1. A sustained, organized public effort making collective claims on target audiences: let us call it a ''campaign''; 2. Employment of combinations from among the following forms of political action: creation of special purpose associations and coalitions, public meetings, solemn processions, vigils, rallies, demonstrations, petition drives, statements to and in public media, and pamphleteering; call the variable ensemble of performances the ''social-movement repertoire''. and 3. Participants' concerted public representations of worthiness, unity, numbers and commitment (WUNC) on the part of themselves and/or their constituencies: call them ''WUNC displays''.

A campaign always links at least three parties: a group of claimants, some object(s) of claims, and a public of some kind. Social movement repertoires are the context-specific, standard operating procedures of social movements, such as: public meetings, solemn processions, vigils, rallies, special-purpose associations and coalitions, demonstrations, petition drives, and pamphleteering. As for WUNC displays, Tilly writes, “The term WUNC sounds odd, but it represents something quite familiar.” Social movements’ displays of worthiness may include sober demeanor and the presence of clergy and mothers with children; unity is signaled by matching banners, singing and chanting; numbers are broadcast via signatures on petitions and filling streets; and commitment is advertised by braving bad weather, ostentatious sacrifice, and/or visible participation by the old and handicapped. WUNC matters because it conveys crucial political messages to a social movement’s targets and the relevant public.

Tilly distinguishes between three sorts of claims advanced by social movements:

1. ''Identity'' claims declare that “we”—the claimants—constitute a unified force to be reckoned with. Such claims commonly include a name for “us,” such as “Cherokees,” “Diamond Cutters,” or “Citizens United against X.” 2. ''Standing'' claims assert ties and similarities to other political actors, for example as excluded minorities, established traders, properly constituted citizens' groups, or loyal supporters of the regime. 3. ''Program'' claims involve stated support for or opposition to actual or proposed actions by the objects of movement claims. The relative salience of identity, standing, and program claims varies significantly among social movements, among claimants within movements, and among phases of movements.

Tilly locates the origin of social movement repertoires in the national regime within which they operate. Defining regimes as the degree of governmental capacity and democracy within a given polity, Tilly argues that contentious repertoires are shaped by regimes in three ways: regimes control claim-making repertoires—determining zones of prescribed, tolerated, and forbidden repertoires; regimes constitute potential claimants and potential objects of claims; and regimes produce streams of issues, events, and governmental actions around which social movements rise and fall.

Tilly also argues that there exists a complex relationship between social movements and democratization. Democratization promotes the formation of social movements, but by no means do all social movements advocate or promote democracy. The distinction is crucial. Tilly cautioned against the illusion that social movements themselves promote democracy by analytically separating movement claims from movement consequences. A pro-democracy movement may lead to anti-democratic consequences, he argued; an example would be anarchists ultimately promoting the fragmentation of democracy-seeking coalitions. Conversely, an anti-democracy movement may promote democratic outcomes by stimulating democratic counter-action by other citizens or self-serving countermeasures by public officials; an example would be unsuccessful anti-immigrant movements.

However, democratization always promotes the formation of social movements because each of its elements contributes to social movement activity. Democratization’s formation of more regular and categorical relations between governments and subjects renders the making of rights-based claims feasible, visible, and attractive. The broadening of rights and obligations within public politics promotes participation in campaigns, social movement performances, and WUNC displays. The equalization of rights and obligations within public politics strengthens cross-category coalitions and the assertion of new identities. And increases in binding consultation of subjects with regard to government policy hold out the prospect of movements acquiring some say in governmental decision making.

In sum, social movements thrive on the secure rights of assembly, association, and collective voice granted by democracy.

Urban Sociology

In the 1960s and 1970s, Tilly also studied migration to cities, and was an influential theorist about urban phenomena and treating communities as social networks. In 1968 Tilly presented his report on European collective violence to U.S. 1968 Kerner Commission, a body formed under the Johnson administration to assess urban unrest amidst the Civil Rights movement. As informed by his studies of contentious politics in 19th century Europe, and the present violence in the U.S., his interest in cities and communities became closely linked with his passion for the study of both social movements and collective violence..

Honours

Tilly received several awards including the Common Wealth Award in sociology in 1982, the Amalfi Prize for Sociology and Social Sciences in 1994, the Eastern Sociological Society's Merit Award for Distinguished Scholarship in 1996, the American Sociological Association's Career of Distinguished Scholarship Award in 2005, the International Political Science Association's Karl Deutsch Award in Comparative Politics in 2006, the Phi Beta Kappa Sidney Hook Memorial Award in 2006 and the Social Science Research Council's Albert O. Hirschman Award in 2008. He also received honorary doctorates from Erasmus University of Rotterdam in 1983, the Institut d'Etudes Politiques of University of Paris in 1993, the University of Toronto in 1995, the University of Strasbourg in 1996, the University of Geneva in 1999, the University of Crete in 2002, the University of Quebec at Montreal in 2004 and the University of Michigan in 2007. In 2001, Columbia’s sociology graduate students named Tilly the Professor of the Year.

Partial bibliography

:''See also Tilly's home page:

''The Vendée: A Sociological Analysis of the Counter- revolution of 1793.'' (1964)

"Clio and Minerva." Pp. 433–66 in ''Theoretical Sociology'', edited by John McKinney and Edward Tiryakian. (1970)

"Collective Violence in European Perspective." Pp. 4–45 in ''Violence in America,'' edited by Hugh Graham and Tedd Gurr. (1969)

"Do Communities Act?" Sociological Inquiry 43: 209-40. (1973)

''An Urban World.'' (ed.) (1974).

''The Formation of National States in Western Europe'' (ed.) (1975)

''From Mobilization to Revolution'' (1978)

''As Sociology Meets History'' (1981)

''Big Structures, Large Processes, Huge Comparisons'' (1984)

''The Contentious French'' (1986)

''Coercion, Capital, and European States, AD 990-1990'' (1990)

''Coercion, Capital, and European States, AD 990-1992'' (1992)

''European Revolutions, 1492–1992'' (1993)

''Cities and the Rise of States in Europe, A.D. 1000 to 1800'' (1994)

''Popular Contention in Great Britain, 1758-1834'' (1995)

''Roads from Past to Future'' (1997)

''Work Under Capitalism'' (with Chris Tilly, 1998)

''Durable Inequality'' (1998)

''Transforming Post-Communist Political Economies'' (1998)

''Dynamics of Contention'' (with Doug McAdam and Sidney Tarrow) (2001)

''The Politics of Collective Violence'' (2003)

''Contention & Democracy in Europe, 1650-2000'' (2004)

''Social Movements, 1768-2004'' (2004)

''From Contentions to Democracy'' (2005)

''Identities, Boundaries, and Social Ties'' (2005)

''Trust and Rule'' (2005)

''Why?'' (2006)

''Oxford Handbook of Contextual Political Analysis'' (2006)

''Contentious Politics'' (with Sidney Tarrow) (2006)

''Regimes and Repertoires'' (2006)

''Democracy'' (2007)

''Credit and Blame'' (2008)

''Contentious Performances'' (2008)

''Social Movements, 1768–2008, 2nd edition'' (with Lesley Wood, 2009)

References

External links

Annotated Links to Charles Tilly Resources

Tributes to Charles Tilly written by his colleagues

Albert O. Hirschman Prize (2008)

Hirschman Prize Ceremony Speeches and Memorial Conference Papers

Charles Tilly and Louise Tilly Fund for Social Science History

Newspaper Obituaries to Charles Tilly

Interactive Version of "Memorials to Credit & Blame" (2008)

Tilly’s Writings on Methodology

'Mechanisms in Political Processes', 2000 article, PDF online

'War Making and State Making as Organized Crime, In ''Bringing the State Back In'', edited by Peter Evans, et al., 169–87. Cambridge, UK: Cambridge University Press, 1985, PDF Online

Charles Tilly's Change Theory

Ideas The ideas interview: Charles Tilly, in The Guardian

Violence, Terror, and Politics as Usual, Boston Review

Predictions: a series of three emails written by Professor Tilly in the week following September 11

Social Scientist Charles Tilly Joins Columbia Faculty, Columbia Press Release

"How I Work" by Charles Tilly

video interview with Chuck Tilly on his work

Category:American sociologists Category:American political scientists Category:Revolution theorists Category:Columbia University faculty Category:The New School faculty Category:People from Elmhurst, Illinois Category:Alumni of the University of Oxford Category:Harvard University alumni Category:Harvard University faculty Category:University of Delaware faculty Category:University of Michigan faculty Category:University of Toronto faculty Category:1929 births Category:2008 deaths Category:Scholars of nationalism

de:Charles Tilly es:Charles Tilly fr:Charles Tilly it:Charles Tilly nl:Charles Tilly ja:チャールズ・ティリー no:Charles Tilly pl:Charles Tilly ru:Тилли, Чарльз zh:查爾斯·堤利

Coordinates	16°40′0″N96°11′0″N
name	Judea Pearl
birth place	Tel Aviv, British Mandate for Palestine (present-day Israel)
nationality	IsraeliAmerican
field	Computer Science Statistics
alma mater	Technion, Israel Rutgers University, U.S. Polytechnic Institute of Brooklyn, U.S.
known for	Artificial Intelligence Causality Bayesian Networks
religion	Jewish
footnotes	}}

Judea Pearl (born 1936) is a computer scientist and philosopher, best known for developing the probabilistic approach to artificial intelligence and the development of Bayesian networks (see the article on belief propagation). He is also credited for developing a method of causal and counterfactual inference based on structural models (see article on causality).

Judea Pearl is the father of journalist Daniel Pearl, who was kidnapped and murdered by militants in Pakistan connected with Al-Qaeda and the International Islamic Front in 2002 for his being American and Jewish.

Biography

Judea Pearl received a B.S. degree in Electrical Engineering from the Technion, Israel, in 1960, a Master degree in Physics from Rutgers University, U.S., in 1965, and a Ph.D. degree in Electrical Engineering from the Polytechnic Institute of Brooklyn, U.S., in 1965. He worked at RCA Research Laboratories on superconductive parametric and storage devices and at Electronic Memories, Inc., on advanced memory systems. He then joined UCLA's Henry Samueli School of Engineering and Applied Science in 1970, where he is currently a professor of Computer Science and Statistics and director of the Cognitive Systems Laboratory. He and his wife, Ruth had three children. In addition, , he is a member of the International Advisory Board of NGO Monitor.

Murder of Daniel Pearl

In 2002, his son, Daniel Pearl, a journalist working for the Wall Street Journal was kidnapped and murdered in Pakistan, leading Judea and the other members of the family and friends to create the Daniel Pearl Foundation. On the seventh anniversary of Daniel's death, Judea wrote an article in the Wall Street Journal titled ''Daniel Pearl and the Normalization of Evil: When will our luminaries stop making excuses for terror?''.

Research

Judea Pearl was one of the pioneers of Bayesian networks and the probabilistic approach to artificial intelligence, and one of the first to mathematize causal modeling in the empirical sciences. His work is also intended as a high-level cognitive model. He is interested in the philosophy of science, knowledge representation, nonstandard logics, and learning. Pearl is described as "one of the giants in the field of artificial intelligence” by UCLA computer science professor Richard Korf. His work on causality has "revolutionized the understanding of causality in statistics, psychology, medicine and the social sciences" according to the Association for Computing Machinery.

Books

''Heuristics'', Addison-Wesley, 1984

''Probabilistic Reasoning in Intelligent Systems'', Morgan-Kaufmann, 1988

''Causality: Models, Reasoning, and Inference'', Cambridge University Press, 2000

''I Am Jewish: Personal Reflections Inspired by the Last Words of Daniel Pearl'', Jewish Lights, 2004.

Scientific papers

List of papers on Pearl's personal website

Lectures

"The Algorithmization of Counterfactuals" video of the Rumelhart Lecture given July 21, 2011.

"On Causes and Counterfactuals" video of a lecture given at a tribute sympoium, March 12, 2010

Honorary Doctorate of Science degree received from the University of Toronto - commencement speech given June 21, 2007

"The Art and Science of Cause and Effect": a slideshow and tutorial lecture by Judea Pearl

Reasoning with Cause and Effect

Awards

2011--IEEE AI's Hall-of-Fame (August, 2011). (All recipients)

2011--David E. Rumelhart Prize (For Contributions to the Theoretical Foundations of Human Cognition)

2011--Rumelhart Prize Symposium in honor of Judea Pearl

2010--Festschrift and symposium in honor of Judea Pearl

2008--Benjamin Franklin Medal in Computers and Cognitive Science

2007--University of Toronto, Honorary Doctorate

2006--Purpose Prize

2004--2003 ACM Allen Newell Award

2003--Pekeris Memorial Lecture

2002—Corresponding Member, Spanish Academy of Engineering

2001--Lakatos Award, London School of Economics and Political Science

AAAI Classic Paper Award">2000--AAAI Classic Paper Award

IJCAI Award for Research Excellence in Artificial Intelligence">1999--IJCAI Award for Research Excellence in Artificial Intelligence

1996--UCLA 81st Faculty Research Lecturer

1995—Member, National Academy of Engineering

1990—Fellow, American Association of Artificial Intelligence (AAAI)

1988—Fellow, Institute of Electrical and Electronic Engineers (IEEE)

1975—NATO Senior Fellowship in Science

1965—RCA Laboratories Achievement Award

References

External links

Judea Pearl's personal website

Daniel Pearl Foundation Website

Interview with Judea Pearl from the U.S. Holocaust Memorial Museum

Interview with Judea Pearl on Robots and Free Will

Category:1936 births Category:Living people Category:Artificial intelligence researchers Category:Jewish American scientists Category:Fellow Members of the IEEE Category:Fellows of the Association for the Advancement of Artificial Intelligence Category:University of California, Los Angeles faculty Category:Israeli computer scientists Category:Israeli philosophers Category:Rutgers University alumni

es:Judea Pearl fr:Judea Pearl