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Information about CausalArguments

Published on November 26, 2007

Author: WoodRock


CAUSALITY:  CAUSALITY A causal claim =df. A claim which says or implies that one thing causes another. A causal hypothesis =df. A causal claim offered to explain the cause or effect of something, when the cause or effect hasn’t yet been conclusively established. A causal argument =df. An attempt to support a causal claim or hypothesis. (See examples of causal claims on page 420. Notice how one claim is put in negative form thus denying that one thing has a causal relation to another.) ONLY-RELEVANT-DIFFERENCE I:  ONLY-RELEVANT-DIFFERENCE I Only-relevant-difference reasoning makes the following points as it concerns reaching a conclusion that one specific event or occurrence caused some other specific event or occurrence: 1. One item has a feature that other similar items or things of the same kind lack. This is called ‘the feature in question.’ 2. There is only one other relevant difference between the thing that has the feature in question and the other items that don’t have the feature in question. 3. Therefore, that relevant difference is the cause of the feature in question. EXAMPLE I:  EXAMPLE I One cut rose lasted longer than another cut rose. The longer-lasting rose was treated with aspirin. Therefore, the aspirin caused the first rose to last longer. One thing. Another thing of the same kind. (Side-by-side comparison.) Similar things, or things of the same kind. The feature in question. The only relevant difference. (The two roses were cut from the same bush, put in the same kind of container, given water from the same source, put in the same location so as to get the same light, etc.) The cause. EXAMPLE II:  EXAMPLE II Jane’s computer used to take a couple of minutes to save large files, particularly those with graphics. Then she doubled the size of the computer’s RAM, and the same files now take about one-fourth the time to save. Therefore the cause of the faster time of saving files was due to increasing the computer’s RAM. One thing. Similar things, or things of the same kind. The same thing at a later time after a change, or similar things which are different stages of the same thing. (Before-after comparison.) The feature in question – faster saving time. Only relevant difference. The cause. ONLY-RELEVANT-DIFFERENCE II:  ONLY-RELEVANT-DIFFERENCE II Only-relevant-difference reasoning requires at least two things to compare. The things compared must be the same except for: 1. The feature in question that we want to know the cause of (e.g. the longer life of the rose, the faster saving time of the computer); and 2. The difference that produced the feature (e.g. the aspirin, the increased RAM). ONLY-RELEVANT-DIFFERENCE III:  ONLY-RELEVANT-DIFFERENCE III Note the term “only” in “only-relevant-difference.” For this kind of reasoning to work, it must be the case that what is identified as the cause of the feature in question is the only relevant difference, and not just a relevant difference amongst others that may contribute to the feature. M&P: “Critical thinking requires consideration of important alternative differences that may have been overlooked.” For instance, “The rose with aspirin might have come from a hardier bush, or it might have been fresher to begin with.” ONLY-RELEVANT-DIFFERENCE IV:  ONLY-RELEVANT-DIFFERENCE IV M&P: “Only-relevant-difference reasoning can be conclusive – at least in experimental conditions” (where things are very carefully controlled). (See the example on page 422.) M&P: “Even apart from carefully controlled experimental conditions, this pattern of reasoning can yield conclusions that are certain by everyday standards.” (See the example on page 423.) However, “Often we cannot be certain that the difference in question is the only relevant difference between the cases we are comparing.” RELEVANCE I:  RELEVANCE I A difference is relevant when “it is not unreasonable to suppose that the difference might have caused the feature in question.” Thus it is not unreasonable to suppose that the aspirin might have caused the rose to last longer, and it is certainly not unreasonable to suppose that the memory boost caused the computer to save files faster. M&P: “The more you know about a subject, the better you are to whether a given difference is relevant to a feature in question.” RELEVANCE II:  RELEVANCE II If you “know nothing about anything” then you are not in a position to determine if a difference is relevant to causing the feature in question. Say, for instance, that when Jane’s computer received additional memory that the technician cleaned the casing of the computer. If you know nothing about anything then you could not conclude that cleaning the casing was not a relevant difference which might be the cause of the faster saving time of the computer. M&P: “However, it is not the mark of a critical thinker to pretend to know nothing about anything.” RELEVANCE III:  RELEVANCE III M&P: “To say that a factor is relevant is only to say that it is not unreasonable to suppose that it caused some feature.” It is not unreasonable to suppose that increasing the RAM of a computer increases the speed with which it saves files. It is unreasonable to suppose that cleaning the computer’s container increased that speed. M&P: “If you learn that, in fact, it did not cause the feature, that doesn’t necessarily mean you were mistaken to think it relevant. Even though it did not cause the feature, it might not have been unreasonable to suppose that it did.” Maybe the aspirin did not cause one rose to last longer than the other, maybe it had been fertilized before being cut and the other had not. Even so, it was not unreasonable to suppose that the aspirin was the cause of the feature in question. ONLY-RELEVANT-COMMON-THREAD I:  ONLY-RELEVANT-COMMON-THREAD I Only-relevant-common-thread reasoning (common-thread reasoning) concerns multiple occurrences of something. For instance, a number of trees in a forest die at the same time. When we try to determine what caused the trees to die we look for the common thread which caused the feature in question (the trees’ dying). The pattern of only-relevant-common-thread reasoning is: 1. Multiple occurrences of a feature (the feature in question) are united by a single relevant common thread (the common thread in question). 2. Therefore, the common thread in question is the cause of the feature in question. ONLY-RELEVANT-COMMON-THREAD II:  ONLY-RELEVANT-COMMON-THREAD II It is possible that more than one common thread exists. In that case there is more than one possible cause. For instance, one common thread could be drought, another could be damaging winds, a third could be disease. M&P: “Common-thread reasoning is best for forming hypotheses which are to be tested in some other way, usually through experimentation involving relevant-difference reasoning.” This is because “multiple occurrences of a feature in question could always have resulted from different causes.” Different instances of the multiple occurrences of tree death could have had different causes – one tree dies from drought, another from wind, and a third from disease. ONLY-RELEVANT-COMMON-THREAD III:  ONLY-RELEVANT-COMMON-THREAD III Common-thread reasoning is also best suited for forming hypotheses because “even if we are dealing with multiple occurrences of an effect somehow known to have been caused by one and the same thing, these multiple occurrences are likely to have many other things in common besides whatever it was that caused them.” And “common-thread reasoning cannot by itself tell us which of these common things was the actual cause of the multiple occurrences of the effect.” Thus the trees in the forest which died (the multiple occurrences) likely had many things in common. Because of this common-thread is better for forming a hypotheses about the cause of death which might be identified by only-relevant-difference reasoning. MISTAKES IN RELEVANT- DIFFERENCE REASONING I:  MISTAKES IN RELEVANT- DIFFERENCE REASONING I 1. The difference taken to be the cause of the feature in question might not be a relevant difference. (Recall that a difference is relevant when it is not unreasonable to think that it played a role in causing the feature in question. One is not thereby committed to saying that it definitely did play a role in causing the feature in question.) For instance, thinking that what caused the deaths of the trees was the noise from a new construction site nearby. MISTAKES IN RELEVANT- DIFFERENCE REASONING II:  MISTAKES IN RELEVANT- DIFFERENCE REASONING II 2. The difference thought to have caused the feature in question might be a relevant difference, but might not be the only relevant difference. (Again a difference is relevant when it is not unreasonable to think that it played a role in causing the feature in question. However, that does not necessarily mean that it did play a role.) M&P: “If the major difference is not the only relevant difference, then it may not be the cause of the feature in question.” Perhaps a farmer’s crop production is much better this year than last year. And perhaps the major difference between this year and last year is more rain. But more rain may not be the only relevant difference – perhaps there was more sun too, and the farmer used a new brand of fertilizer. Then more rain might not be the cause of the better crops. MISTAKES IN RELEVANT- DIFFERENCE REASONING III:  MISTAKES IN RELEVANT- DIFFERENCE REASONING III 3. The difference being considered as the cause of something may in fact be the effect rather than the cause. For instance, George has not slept well the past couple of nights, and he has also been nervous the past couple of days. He may think that his not sleeping well (difference considered as cause) is the cause of his nervousness (the feature in question), while the truth is that his nervousness is the cause of his not sleeping well. 4. The difference might not cause the feature in question. Instead, the difference and the feature in question are each effects of a third underlying cause. It might be that both George’s nervousness and his insomnia are due to his feelings of guilt about something. MISTAKES IN COMMON-THREAD REASONING I:  MISTAKES IN COMMON-THREAD REASONING I 1. Is the thread identified as common relevant to the feature in question? Suppose that five friends all get sick after eating Jeanie’s pot roast. A thread common to the group, in addition to eating the pot roast, might be that each just finished reading the same book. However, that would not seem to be relevant to their getting sick (the feature in question). And so that particular common thread should not be identified as the cause of the sickness. Better reasoning would be to suppose that eating the pot roast was the common thread which caused the multiple occurrences of illness. MISTAKES IN COMMON-THREAD REASONING II:  MISTAKES IN COMMON-THREAD REASONING II 2. A difference taken to be the cause of the feature in question might be a relevant difference, but is not the only relevant difference. For instance, the friends who got sick after eating Jeanie’s pot roast might also have in common consuming a fair amount of red wine. The consumption of this wine might be relevant to their sickness, but may not be the only thing of relevance to their illness, since something about the pot roast may also be relevant. If there is more than one common thread, then any particular common thread focused on as the cause of the feature in question may only be one cause of the feature in question, and so may only partially explain it. (It may also have nothing to do with it.) MISTAKES IN COMMON-THREAD REASONING III:  MISTAKES IN COMMON-THREAD REASONING III 3. Cause and effect may have been reversed. Perhaps a correlation is noted between education and values in that societies that spend a lot on education tend to have better values than those which do not. It is then hypothesized that spending more on education promotes better values. However, the situation may as a matter of fact be reversed. It may be that societies with better values spend more on education. 4. What is taken to be the common thread and the feature in question may have a common cause. (See the two examples on page 428.) MISTAKES IN COMMON-THREAD REASONING IV:  MISTAKES IN COMMON-THREAD REASONING IV 5. The feature in question might not require a common cause. Maybe the people who got ill after eating Jeanie’s pot roast all got ill for different reasons – too much wine in one case, the flu in a second, an earlier lunch for a third, and so forth. If the causes of the common feature – the illness – were different in each case, then the common thread of eating Jeanie’s pot roast was not the cause of getting ill, but was merely coincidence. M&P: “We should not unthinkingly assume that multiple occurrences of something have a common cause – even if there is a common thread present.” M&P: “A common thread might induce us to assume that a single thing caused the feature in question, and that might be a mistake.” POST HOC, ERGO PROPTER HOC I:  POST HOC, ERGO PROPTER HOC I Post hoc, ergo propter hoc is Latin for “after this, therefore because of this.” The fallacy of post hoc, ergo propter hoc =df. Thinking that x causes y simply because y occurs after x. For instance, thinking that day causes night simply because night follows day, or thinking that because every time that the Yankees win the world series we have a cold winter that the cause of the cold winter is the Yankees winning the world series. POST HOC, ERGO PROPTER HOC II:  POST HOC, ERGO PROPTER HOC II M&P: “It is a mistake to think that, just because y happened around the same time that x happened, that y happened because x happened,” which is why post hoc, ergo propter hoc is a fallacy. It is true that effects follow their causes in time, and so it may be true that x both causes y and y follows x in time. It is just that, it is fallacious to assume a causal relation between x and y based only on the fact that one follows the other. You have good reason to suppose that x caused y if it is the only thing which accounts for y or if it is the best explanation of y. (See examples on pages 429-430.) CAUSALITY VS. COINCIDENCE I:  CAUSALITY VS. COINCIDENCE I The connection between events can be coincidental, not causal. Three common kinds of coincidence are: 1. Two things might not be causally related at all, but are taken to be causally related. For instance, a man has a heart attack and dies after running. The running is taken to be the cause of the heart attack but in fact an autopsy shows that the running has nothing to do with it. Or people theorize that the cause of AIDS (rather than a cause of the spreading of AIDS) is sex. Both examples are post hoc reasoning. M&P: “What underlies many superstitions is thinking of coincidental events as being causally related to one another.” CAUSALITY VS. COINCIDENCE II:  CAUSALITY VS. COINCIDENCE II 2. Multiple occurrences of an effect are thought to be due to a common thread shared by all the occurrences when the truth is that some other common thread caused the occurrences. When this is the case then it is “just coincidence that the first common thread is present.” For instance, the people who got ill after eating Jeanie’s pot roast may have been together a day or two before at a party at which each was exposed to a common virus which took a couple of days to make them sick. Their shared feature of getting sick also had the common thread of eating at Jeanie’s, but that common thread is just coincidence, and, as such, is causally irrelevant. CAUSALITY VS. COINCIDENCE III:  CAUSALITY VS. COINCIDENCE III 3. It can be assumed that multiple occurrences of a particular effect are due to a common thread shared by all the occurrences when in fact the occurrences were not caused by a single thing. In the five people getting ill after eating at Jeanie’s, the multiple occurrences are the five cases of illness and the common thread assumed to be the cause of the illnesses is eating Jeanie’s pot roast. Eating Jeanie’s pot roast may be the common thread which is shared by all five illnesses, and so may be their cause, but it may also simply be a coincidence. The truth may be that each illness has a different cause. CAUSATION IN POPULATIONS:  CAUSATION IN POPULATIONS Some causal claims apply to populations rather than to individuals. For instance, “Smoking causes cancer” is meant to link smoking causally to cancer, not in any particular individual, but to smokers in general. Saying that smoking causes cancer means that, we would expect more cases of lung cancer in populations in which everyone smoked rather than in populations in which no one smoked. M&P: “To say that X causes Y in population P is to say that there would be more cases of Y in population P if every member of P were exposed to X than if no member of P were exposed to X.” KNOWLEDGE OF CAUSES IN POPULATIONS:  KNOWLEDGE OF CAUSES IN POPULATIONS How do we know, or what makes us think, that one thing is a cause of another? Or what is the evidence for the claim that there would be more cases of something (Y) in a population in which every person in the population were exposed to something (X) than if they were not? The first thing which argues in favor of one thing causing another in a population is controlled cause-to-effect experiments. CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS II:  CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS II Controlled cause-to-effect experiments involve randomly dividing a random sample of a target population into two groups: an experimental group and a control group. In the experimental group all members of the group are exposed to something c which is suspected to cause something else. (For instance, being exposed to some mold which is thought to cause a certain allergy.) The members of the control group are not exposed to c. However, other than this difference, members of the control group are treated exactly the same as members of the experimental group. (Thus the members of this group are not exposed to the mold, but otherwise everything else is the same.) CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS III:  CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS III The the experimental and control groups are “then compared with respect to frequency of some effect, e.” (Here the occurrence of the allergy which the mold is thought to cause.) M&P: “If the difference d, in the frequency of e in the two groups is sufficiently large, then c may justifiably be said to cause e in the population.” Thus if 50 of 100 people in the experimental group get the allergy when exposed to the mold, and only 1 out of 100 people in the control group gets the allergy when not exposed to the mold, then the difference - 49% - may be said to be sufficiently large for the mold to cause the allergy in that group. CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS IV:  CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS IV It is a question how large the difference d must be between the experimental group and the control group to say that a certain effect e is due to a certain cause c. This is determined in relation to the size of each group, say 100 people, at some approximate statistically significant level, say 0.05, which means that “the result could have arisen by chance in about 5 cases out of 100.” For instance, to think that e is due to c in a group of 100 people, the difference d between the number of people in the experimental group with e and those in the control group with e must exceed 13. (For further see table on page 447, and note that, as the size of the population goes up, the figure that d must exceed goes down.) CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS V:  CONTROLLED CAUSE-TO-EFFECT EXPERIMENTS V The sample of the population from which the members of both the experimental and control groups are taken should be representative of the target population. Accordingly, the sample used to construct each group should be taken at random. In addition, the assignment of a member of the sample to either the experimental or the control group should also be done at random. SAMPLE SIZE:  SAMPLE SIZE One should not automatically assume that a sample size in controlled experiments is “large enough to guarantee significance.” M&P: “A large sample is no guarantee that the difference (d) in the frequency of the effect in the experimental group and in the control group is statistically significant” – a particular experiment may not achieve statistically significant results even with a large sample size. M&P: “However, the larger the sample, the smaller d – expressed as a difference in percentage points – need be to count as significant.” (Refer to table on page 447.) FREQUENCY DIFFERENCE:  FREQUENCY DIFFERENCE One must also not automatically assume that the difference in frequency between experimental group and control group of an effect being investigated “is great enough to guarantee significance.” A difference may seem great that is not really statistically significant. For instance, “If there are 50 rats in an experimental group and 50 more in a control group, then even if the frequency of skin cancer found in the experimental group exceeds the frequency of skin cancer found in the control group by as much as 18 percentage points, [say 2 rats in the control group and 11 in the experimental group got cancer] this finding would not be statistically significant (at the 0.05 level).” (This is 9 rats in 50, and where we would expect that 2 rats [0.05] might get cancer by chance.) ANALOGICAL EXTENSION:  ANALOGICAL EXTENSION M&P: “The results of controlled experiments are often extended analogically from the target population (e.g. rats) to another population (e.g. humans).” We need to be careful here, since we would need to know how representative the rats used in each group are of all rats; how many features relevant to the experiment’s conclusion human beings have in common with rats so that the conclusion of the rat study can be reasonably applied to humans; and “whether there are important relevant differences between the target population in the experiment [the rats] and the population to which the results of the experiment are analogically extended [humans]. REPUTABLE SOURCES:  REPUTABLE SOURCES M&P: “In reputable scientific experiments it is safe to assume that randomization [of the sample from the target, and the division of members of the sample into the two groups – control and experimental] has been employed, but one must be suspicious of informal ‘experiments’ in which no mention of randomization is made.” M&P: “Any outfit can call itself the ‘Cambridge Institute for Psychological Studies’ and publish its own ‘journal.’” However, such an outfit “could consist of little more than a couple of university dropouts with a dubious theory and an axe to grind.” NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES I:  NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES I In a non-experimental cause-to-effect study members of a target population, such as humans, who have not yet shown evidence of a suspected effect e, such as allergic reaction, “are divided into two groups that are alike in all respects but one.” M&P: “The difference is that members of one group, the experimental group, have all been exposed to the suspected cause c (e.g. mold), whereas the members of the other group, the control group, have not.” NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES II:  NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES II The difference between non-experimental cause-to-effect studies and controlled cause-to-effect experiments is that “the members of the experimental group are not exposed to the suspected causal agent by the investigators.” This is because there are limits to what is ethically acceptable to expose humans to (and some other creatures?). We “can’t purposely expose human subjects to potentially dangerous agents.” For instance, if smoking is suspected of causing cancer, we would not make people smoke to see if it causes cancer. What we would do is select from a group of people who voluntarily smoke and compare them with people who are otherwise like them who do not smoke. NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES III:  NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES III Just as with controlled experiments, experimental and control groups in non-experimental studies are compared to see how often the effect being looked for appears in each group. M&P: “If the frequency in the experimental group exceeds the frequency in the control group by a statistically significant margin, then we may conclude that c is the cause of e in the target population.” For instance, if more people get lung cancer in the experimental group by a statistically significant margin than do those in the control group, then we can conclude that smoking causes cancer. NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES IV:  NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES IV M&P: “Non-experimental studies are inherently weaker than controlled experiments as arguments for causal claims.” One reason for this is that members of the experimental group who have already been exposed to the cause c in question may differ in some respect r from the control group in addition to c. And it may be that r is a cause of the effect being looked for. For instance, we may think that a fatty diet – c – causes colon cancer – e. Members of the experimental group will have a fatty diet and those of the control group will not. But even if the experimental group has a statistically significant higher rate of colon cancer, it may be due to a different or additional factor, such as heavy drinking, since many people who eat fatty foods also consume too much alcohol. NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES V:  NON-EXPERIMENTAL CAUSE-TO-EFFECT STUDIES V Another problem is incomplete knowledge of the causal complexity of nature and human reality. M&P: “Because we do not have complete knowledge of what factors are causally related to what other factors, it is impossible to say for certain that all possibly relevant variables in such studies have been controlled.” NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES I:  NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES I In a non-experimental effect-to-cause study the ‘experimental’ group already have the effect e – such as lung cancer – being investigated. The experimental group is then compared with a control group none of the members of which have e, and the frequency of the suspected cause c – e.g. smoking cigarettes – is measured. M&P: “If the frequency of c in the experimental group significantly exceeds its frequency in the control group, then c may be said to cause e in the target population.” NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES II:  NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES II One cannot simply assume that, because the sample size seems large enough, or because the difference in frequency seems significant, the findings of the study are significant. One must also be careful about extending the results of a non-experimental effect-to-cause study analogically to other populations. For instance, we must be careful in concluding from a group study of something that caused a certain reaction in men that the same thing will cause the same thing in women since there may be relevant differences between the sexes which would interfere with the expected result. NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES III:  NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES III It must also be noted that “the subjects in the experimental group may differ in some important way (in addition to showing the effect – such as lung cancer) from the rest of the target population.” (See the example on page 450.) M&P: “Any factor that might bias the experimental group in such studies should be controlled. If, in evaluating such a study, you can think of any factor that has not been controlled, you can regard the study as having failed to demonstrate causation.” NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES IV:  NON-EXPERIMENTAL EFFECT-TO-CAUSE STUDIES IV M&P: “Effect-to-cause studies show only the probable frequency of the cause, not the effect, and thus provide no grounds for estimating the percentage of the target population that would be affected if everyone in it were exposed to the cause.” APPEAL TO ANECDOTAL EVIDENCE I:  APPEAL TO ANECDOTAL EVIDENCE I If we try to argue that, because we know of a case or two in which one thing caused another, then we are appealing to anecdotal evidence in accounting for the causal relation. For instance, we might say that aunt Velma has begun each day with a shot of whiskey for the last 60 years, and then conclude that drinking a shot of whiskey in the morning makes you live longer. M&P: “To establish that x is a causal factor for y we have to show that there would be more cases of y if everyone did x than if no one did, and you can’t really show this – or demonstrate that x isn’t a causal factor for y [see next slide] – by citing an example or two. APPEAL TO ANECDOTAL EVIDENCE II:  APPEAL TO ANECDOTAL EVIDENCE II If, on the other hand, we try to argue that, because we know of a case or two in which one thing failed to cause another, then we are appealing to anecdotal evidence in accounting for the lack of causal relation. For instance, a person might maintain that heavy drinking of alcohol does not cause liver damage since his uncle drank 10 beers a day for 60 years and died from getting hit by a kid on a skateboard. And his autopsy showed that his liver was normal for a man his age.

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