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hapter C 11 Overview T wo kinds of explanation are addressed in Chapter 11: physical causal and behavioral causal. When physical phenomena need explaining, the hypothetical causes are drawn from a physical background, e.g. "The reason the paper is on the lawn is because the wind blew it t here." When behavioral phenomena need explaining, the hypothetical causes are drawn f rom a psychological background, e.g. "The reason she went to the Dairy Barn was because she desired ice cream." A n explanation's adequacy is relative to what one is looking for. Nevertheless, it shouldn't be unnecessarily complicated, circular, inconsistent, incompatible with fact or t heory, vague, or untestable in principle. I t shouldn't generate meaningless predictions, false predictions, or no predictions at all. T he general strategy for forming causal hypotheses (or educated guesses) is called " inference to the best explanation." Observing an association, or co-variation, between two events can serve as a beginning. Infer ring a causal connection, however, requires r igorous application of the Methods of Agreement and Difference while being guided by background k nowledge of causal mechanisms. Finding a hypothesis that adequately explains the facts can be like diagnosing a disease or solving a crime. The Best Diagnosis Method gathers as m any "symptoms" as possible, t r ies to sift out the i r relevant ones, and t r ies to find the s trongest connections. Controlled cause-to-effect experiments are the most direct way of confirming a causal hypothesis. By repeating an experiment and systematically eliminating other possible causes and getting the same effect, the hypothesis becomes confirmed. Indirect methods of testing causal hypotheses are more appropriate for human populations, for p ractical and ethical reasons. These studies compare a group of people who exhibit the effect under investigation with a control group who do not have it. Animal experiments are another way to avoid testing humans directly. The results may be applied to humans by analogical reasoning. T here are many, and varied, ways of making mistakes in causal reasoning. Some of t he most prominent involve believing there is a causal connection between A and B when actually the relationship is: coincidental, a result of a third underlying cause, or reversed. T he law relies on establishing a causal relationship between an action and the r esulting harm. Whether or not someone is held liable for something depends on whether or not the harm can be t raced back to his/her action as the proximate cause. 1. Explanations are different than arguments. There are two kinds of e xplanations: physical causal and behavioral causal. a. You use arguments to support a statement but explanations to elucidate the r easons why some event happened. i. When we give a reason for doing something, we are presenting an argument for doing it. i i. When we cite an individual person's reason for doing it, we are explaining w hy s/he did i t. b. If the causal explanation of an event refers to physical background information, t hen the explanation is physical causal, e.g. "The main cause of global warming is an increase in the concentration of greenhouse gases resulting from human activity." c. If the causal explanation of an event is explained in terms of motives or reasons, t hen the explanation is behavioral causal, e.g. "The reason for poverty in capitalist societies is class struggle." 2. Explanatory adequacy is a relative concept. a. What counts as an adequate causal explanation depends on our circumstances and needs. At minimum, an explanation should be consistent, not conflict with established fact or theory, be testable, and not have logical errors, such as circularity or unnecessary complexities. b. Testability is the most important feature of an adequate explanation. i. Causal explanations generate expectations, or predictions. Non-testable explanations generate meaningless predictions, no predictions, or false p redictions. The test for explanatory adequacy is seeing if the predictions t urn out to be t rue. i i. Some predictions cannot be tested because of practical limitations, but others a re untestable even in principle. I t is only the latter that should be abandoned. i ii. Some explanations are not adequate because they are circular, i.e. they simply restate themselves and so generate no meaningful predictions. Others a re not adequate because they are unnecessarily complex and so contain elements in which there is no reason to believe. 3. Methods of forming causal hypotheses include the Method of Difference, t he Method of Agreement, and the Best Diagnosis Method, all of which are g uided by background knowledge of Causal Mechanisms. a. The Method of Difference i dentifies an event X as the only relevant difference (or one of the relevant differences) that has brought about the effect Y. i. More precisely, we say that one i tem has a feature that other i tems lack (the feature in question), and that only one relevant difference (the difference in question) distinguishes the i tem with the feature from the i tems without the feature; the difference in question then causes the feature in question. i i. To make such an argument we need to know about at least two circumstances, one in which Y occurs and one in which it does not. If X is p resent along with Y and absent when Y is, then X might cause Y. i ii. "I ate my usual breakfast today, but with bacon instead of my usual sausage, and now I feel thi rsty. Bacon tastes saltier than sausage, so I think the bacon m ade me thi rsty." 1. The bacon with breakfast is being put forward as the only occurr ing d ifference. 2. Notice that the speaker is claiming some relevance to this difference. Bacon tastes saltier than sausage, and we know that salty foods can make us thirsty. iv. Arguments from an only relevant difference can be as conclusive as any kind of reasoning we know. 1. If you walk into a room, f l ip a switch beside the door, and see the lights go on, you conclude that f l ipping the switch caused the light to go on the basis of relevant difference reasoning. 2. Even less indubitable arguments about the only relevant difference can p rovide as much certainty as ordinary experience ever provides, as long as t he difference in question is t ruly relevant. b. The Method of Agreement l inks a cause to the feature in question on the g rounds that it is the only (or one of the only) relevant common feature(s) among possible causes of Y. A correlation between two events provides a good starting point for hypothesizing. i. In such an argument, we begin by noticing that the feature in question (Y) occurs more than once, and that some common feature X is present on every occasion. i i. Such reasoning requires that we know of more than one circumstance in w hich Y occurs. i ii. Co-variation is what happens when changes in one phenomenon are accompanied by changes in another phenomenon. Co-variation suggests, but does not confi rm, that causation may be present. iv. Fallacies of logic occur when thinking that correlations or co-variation prove causation. 1. Cum hoc, ergo propter hoc is the fallacy of assuming a cause and effect r elationship between co-varying phenomena. 2. Post hoc, ergo propter hoc is the fallacy of assuming a cause and effect r elationship between phenomena just because one event occurred before another. 3. These are both fallacies of causal reasoning because they do not eliminate t he possibili ty of coincidence, an underlying cause, or confusion between cause and effect. c. Causal Mechanisms a re the interfaces between a cause and i ts effect. Background knowledge of "what causes what" is necessary to decide what is r elevant to consider as a possible cause and what is not. i. We should be guided by our background knowledge of how things work, i.e. causal mechanisms, but humble enough to abandon a hypothesis when the r elationship between one event and another proves to be something other t han causation. i i. Ut ilizing knowledge of causal mechanisms, the Method of Difference, and the Method of Agreement together facilitate hypothesis formation. d. The Best D iagnosis Method is a way to find a hypothesis, much like solving a crime or diagnosing a medical condition. i. Coming up with a hypothesis or "diagnosis" by this method involves assembling "symptoms" or "clues" and looking for patterns of association. i i. The next step is to ascertain which are the relevant symptoms and the s trongest associations between them and possible causal mechanisms. The best hypothesis is the one that eventually gets confirmed. 4. A causal claim says that one thing causes another; a hypothesis is an initial speculation about a causal claim involving I nference to the Best E xplanation. a. The general strategy for forming a hypothesis is "Inference to the Best E xplanation." We begin by making the best guess and some alternative guesses. b. If we were reasoning Inference deductively, to the Best Explanation would constitute the fallacy of affi rming the consequent. In inductive reasoning, however, on the basis of assumed probabilities of alternative explanations, i t is not a fallacy to form an initial hypothesis this way; in fact, it may be all that we can do. 5. Confi rming a causal hypothesis consists in r igorous application of the a bove methods. Once the Method of Agreement or Disagreement suggests a h ypothesis we can begin to eliminate other possibilities a. Cont rolled cause-to-effect experiments t ry to show d irectly t hat the presence and absence of a suspected cause, C, yields different frequencies, d , of the observed effect, E. i. By the Method of Agreement we might form a hypothesis as to what caused something to happen, e.g. that heat caused the water to boil, but the r elationship between heat and boiling could just be a coincidence. The next s tep is to eliminate the other possibilities. i i. Repeating the experiment many times is the backbone of the scientific method. If the water continues to boil every t ime we apply heat, we are closer t o confi rming the hypothesis. Using the Method of Difference, we eliminate t he possibili ty that the type and composition of the pan it is boiled in (aluminum, iron, Teflon, etc.) had any effect on the outcome. When repeated t r ials, regardless of the composition of the pan, result in the same effect, we can confirm that heat was the cause of boiling. i ii. In essence, hypothesis confi rmation is really just careful application of the Method of Agreement combined with the Method of Difference. Observing t hat the water boils when heat is applied is the Method of Agreement, i.e. t hat heat and boiling always occur together. Observing that the only d ifference between its boiling and not boiling is the application of heat is the Method of Difference. b. Causation in human populations d iffers significantly from causation among specific events and needs alternate argumentative strategies. i. A n onexperimental cause-to-effect study t r ies to establish causation in populations, but with methods and standards that avoid direct experimentation on subjects. 1. This type of study reasons forward from a possible cause, C, to the observed effect, E. People subjected to the suspected causal agent are compared to a "control" group who has not been subjected to the same suspected causal agent in order to see if the frequency, d, of a possible effect is greater in the first group. If the frequency is significantly higher i n the target population than the control group, we conclude that C caused E. 2. Because we can never be sure that factors other than the hypothesized cause contr ibute significantly to the effect, these studies are not nearly as conclusive as controlled experimental studies. ii. A n onexperimental effect-to-cause study a lso avoids direct experimentation on subjects. 1. This type of study reasons backwards from an existing effect to its possible cause (or to one causal factor). This time investigators begin with a g iven effect, E; they select an experimental group that exhibits E and a control group not exhibiting E. Members of both groups are inspected for exposure to C, the suspected cause. If the frequency of C in the experimental group significantly exceeds the frequency in C, we call C a cause of E in the target population. 2. The same cautions about nonexperimental cause-to-effect studies also apply here. As before, the members of the experimental group may differ r elevantly from the rest of the target population. If we begin by studying people with arthr i tis, we must first recall that they will be older than an average member of the population. Again, we adjust the control group to r esemble the experimental group. Again, if you can think of other factors t hat could have inf luenced C, make sure the control group was adjusted to r eflect them. 3. One final alert about effect-to-cause studies: They are less useful in m aking causal predictions about the population. Effect-to-cause studies show only the probable frequency of the cause in cases of a given effect, not the probable frequency of the effect in cases of a given cause. T herefore, they don't permit us to say what percentage of the target population would display E if everyone were exposed to C. Ideally, we would follow such a study with a cause-to-effect study, watching people w ith C over a long period to see if they develop E. iii. Experimenting on animals is another method of testing causal hypotheses t hat avoids experimenting on humans, but the experimental results apply to h umans by analogical reasoning (Chapter 10). 6. M istakes in causal reasoning compromise our ability to make predictions a nd hold reasonable expectations. Some examples of causally defective r easoning follow. a. Both cum hoc, ergo propter hoc and post hoc, ergo propter hoc fail to establish the i mprobability of the following three possibilities: that the connection between C and E is due to coincidence, that C and E both result from a third u nderlying cause, and that E caused C rather than the other way around, i.e. confusing effect with cause. b. Confusing effect with cause in medical tests i s a common, and serious, mistake. O ften the chances of actually having the condition are erroneously exaggerated. e.g. What are the chances that you have a disease if you test positive for i t? Testing positive is the effect, E, of having the disease, C. Testing positive on a t est that is 90% accurate means that 90% of those who have C will have E. Say t he base rate (frequency of occurrence in the population at large) of the disease in question is 1%, and 10% who do not have C test positive (false positive), and you t est positive. If you conclude (or your doctor tells you) that your chance of having t he disease is 99%, then you'd be wrong. You'd have switched C and E. Your chances are actually only 8% of having the disease. c. Overlooking statistical regression causes errors in causal reasoning. The s tatistical property of measurements of mean values of populations, called "statistical regression," could be responsible for some effect, rather than causation. Sometimes the explanation for why a basketball player, for instance, returns closer to her average performance after an extremely good (or bad) performance is simply regression to the mean. d. Proof by absence of disproof is a defect of reasoning because the absence of the d isproof of a causal hypothesis does not increase the likelihood that the hypothesis is t rue. The reasons for believing the hypothesis in the fi rst place are left intact with the discovery of no disproof, but that absence doesn't create an additional reason for thinking that the hypothesis is t rue. e. Appeal to anecdote i s really just the fallacy of hasty generalization of post hoc r easoning. Generalizing from an anecdote doesn't really show anything and can easily be countered by finding one example that disproves the generalization. f. Confusing explanations and excuses is a fallacy based on making the erroneous assumption that an explanation of an action is a justification for it. An expert may t ry to explain the psychological-sociological-political motivations behind the 9/11 suicide attacks on the World Trade Center without, in any way, t rying to excuse the act. 7. Causation in the law is the connection between action and ha rm. a. Under the law, if your action causes (or attempts to cause) harm (or contributes t o i ts cause), you are held responsible for that harm. i. Conditio sine qua non, or "but for" causes (Y would not have happened but for X's having happened) are important because it wouldn't be fair to punish someone for causing harm Y by doing X when Y would have happened even if X h ad not been done. i i. Proximate cause i s a restrictive version of "but for" causes, combining fact and policy. With a policy that indicates what's relevantly important, we can t race t he harm caused back to the action against a "causal background", or common recurrent feature in the environment. I t can then be argued that some links in the causal chain of events were, or were not, part of the defendant's responsibility. b. Contravening events factor into legal argument. i. Establishing causation is essential in establishing liability, e.g. if Jeff u nintentionally fails to completely extinguish his campfire and i t burns down t he forest, then he is liable to a certain degree. If Jennifer finds the smoldering fire and pours gasoline on i t and i t burns down the forest, then she is liable to a much greater degree than Jeff was. Jennifer's voluntary i ntervention contravenes Jeff's causal role. i i. Proximate cause helps clarify when someone should not be held liable for being the cause of harm, e.g. if Jamie's muffler unexpectedly falls off his wellmaintained car while driving, and causes a spark, which causes a fire that burns down the forest, Jamie cannot be blamed even though he was part of t he causal chain. ... View Full Document

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