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Casualty of phisiology

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 a sufficient cause of y,presence x guarantees y. However, other events may also cause y, and thus y's presence does not ensure the presence

Mackie argues that usual talk of "cause" in fact refers to 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 presence of oxygen, the flammability of the house, and the absence othese 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 cause of the house burning down.
Causality contrasted with logical implication
Logical conditional statements are not statements of causality. Since logical conditional statements and causal statements are both presented using "If...then..." in English they are commonly confused;distinct, however. The standard conditional statement expresses a fact about the actual world, while causal statements imply something more. For example all of the following statements are true terpreting "If... then..." as the logical conditionalIf George Bush was president of the United States in , then Germany is in Europe If George Washington was president of the United States in thenGermany is in Europe The second and third are both true because the antecedent is false. Of urse, none of these statements express a causal connection between the antecedent and consequent.Another sort of logical implication, counterfactual implication has a stronger connection with causality. However, not even all counterfactual statements count as examples of causality. Consider the following two statements:

In the first case it would not be correct to say that A's being a triangle caused it to have three sides, since the relationship between triangularity and three-sidedness is one of definition. 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, . 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' theoryOne 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 Joand 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 subsantial discussion. Ganeri, Noordhof, and Ramachandran Paul

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 iff A's occurrence increases the probability of B. This is sometimes interpreted to reflect imperfect owledge 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 geneticefect 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 equi in terms of the outcomeso that any changes 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 what 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 stron incompatible with the observed correlations. In general this leaves a set of possible causal relations, which should then be tested by designing experiments. If experimental data is already available, the algorithms can take advantage of that as well. In contrast 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 followup values are known for a theorized cause and effect. addition of time as a variable, 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.