Richard L Epstein

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by Critical Thinking (3rd Edition) (pdf)


  the Littleton, Colo., school shootings on 35 years of liberalism.

  In a speech Wednesday to about 500 Republican women, Gingrich said elimination

  of prayer and "the Creator" from schools, lack of teaching about the Constitution and a

  steady stream of violence in the movies and video games have produced teen-agers who

  are morally adrift. He blamed an overtaxing government for forcing parents to spend

  more time working, away from their children.

  But Gingrich said attempts to make guns a scapegoat for last month's shootings at

  Columbine High School were "banalities." "I want to say to the elite of this country—

  the elite news media, the liberal academic elite, the liberal political elite," Gingrich told

  the Republican Women's Leadership Forum, "I accuse you in Littleton, and I accuse you

  in Kosovo, of being afraid to talk about the mess you have made, and being afraid to

  take responsibility for things you have done, and instead foisting upon the rest of us

  pathetic banalities because you don't have the courage to look at the world you have

  created." . . .

  Gingrich was harsh in placing the blame for the murder of 12 students and a teacher

  in Littleton, Colo., by two classmates. He said the killers probably never realized they

  were robbing the "inalienable rights of life, liberty and the pursuit of happiness" of their victims because the schools never taught them the constitutional meaning of the words.

  "For 35 years, the political and intellectual elites (and) political correctness have

  undermined the core values in American history, so the young people may not know

  who George Washington is, or they may not know who Abraham Lincoln is—but they

  know what MTV is," Gingrich said.

  Gingrich said Republicans should lead a campaign to "expose" movie and video

  game makers to liability lawsuits, and to challenge "Democrats to cut off the fund-

  raising" from makers of violent movies.

  Chuck Raasch, Gannett News Service, USA Today, May 13, 1999

  C. How to Look for the Cause

  I have a waterfall in my backyard in Cedar City. The pond has a thick rubberized

  plastic pond liner, and I have a pump and hose that carry water from the pond along

  the rock face of a small rise to where the water spills out and runs down more rocks

  with concrete between them. Last summer I noticed that the pond kept getting low

  every day and had to be refilled. You don't waste water in the desert, so I figured I'd

  better find out what was causing the loss of water.

  I thought of all the ways the pond could be leaking: The hose that carries the

  water could have a leak, the valve connections could be leaking, the pond liner could

  be ripped (the dogs get into the pond to cool off in the summer), there could be

  cracks in the concrete, or it could be evaporation and spray from where the water

  comes out at the top of the fountain.

  I had to figure out which (if any) of these was the problem. First I got someone

  318 CHAPTER 15 Cause and Effect

  to come in and use a high pressure spray on the waterfall to clean it. We took the

  rocks out and vacuumed out the pond. Then we patched every possible spot on the

  pond liner where there might be a leak.

  Then we patched all the concrete on the waterfall part and water-sealed it. We

  checked the valve connections and tightened them. They didn't leak. And the hose

  wasn't leaking because there weren't any wet spots along its path.

  Then I refilled the pond. It kept losing water at about the same rate.

  It wasn't the hose, it wasn't the connections, it wasn't the pond liner, it wasn't

  the concrete watercourse. So it had to be the spray and evaporation.

  I reduced the flow of water so there wouldn't be so much spray. There was a

  lot less water loss. The rest I figured was probably evaporation, though there might

  still be small leaks.

  In trying to find the cause of the water leak I was using the method scientists

  often use:

  Conjecture possible causes, and then by experiment eliminate them

  until there is only one. Check that one:

  Does it make a difference? If the purported cause is eliminated,

  is there still the effect? Could there be a common cause?

  Not much spray, not much water loss. I couldn't be absolutely sure, but it seemed

  very likely I had isolated the cause.

  The best prophylactic against making common mistakes in reasoning about

  causes is experiment. Often we can't do an experiment, but we can do an imaginary

  experiment. That's what we've always done in checking for validity: Imagine the

  possibilities. But note: This method will help you find the cause only if you've

  guessed it among the ones you're testing.

  Exercises for Section C

  1. Come up with a method to determine whether there's cause and effect:

  a. Pressing the "Door Close" button in the elevator causes the doors to close.

  b. Zoe's belching caused Spot to run away.

  c. Reducing the speed limit to 55 m.p.h. saves lives.

  d. The red-headed lady walking by the classroom causes Professor Zzzyzzx to arrive at

  class on time.

  2. Flo: Isn't it amazing that of all the houses in this town, I was born in one where the

  people look so much like me!

  What is Flo overlooking?

  3. Dick: {Bending over, sweating and cursing) There's something wrong with my bike.

  Zoe: What?

  Dick: Something's going "click, click, click" all the time.

  EXERCISES for Section C 319

  Zoe: Must be something that's moving.

  Dick: Duh. Here, hold it up while I turn the pedals, {click, click, click, . . . )

  Zoe: Yup, there it is.

  Dick: It must be in the pedals or the wheels.

  Zoe: Stop pedaling. . . . It's gone away.

  Dick: It must be in the pedals, then.

  Evaluate how Dick and Zoe have tried to isolate the cause here.

  Tom was asked to bring in a causal claim he made recently and evaluate it. Here's his work.

  The only time I've had a really bad backache is right after I went bicycling early in

  the morning when it was so cold last week. Bicycling never bothered me before.

  So it must be the cold weather that caused my back to hurt after cycling.

  Causal claim: The cold weather caused my back to hurt after cycling.

  Cause: It was cold when I went cycling.

  Effect: I got a backache.

  Cause and effect true! Yes.

  Cause precedes the effect! Yes.

  Valid or strong! I think so.

  Cause makes a difference! Sure seems so.

  Common cause! None.

  Evaluation: The criteria seem to be satisfied. But now I'm wondering if I haven't

  overlooked some other cause. I also had an upset stomach. So maybe it was

  the flu. Or maybe it was tension, since I'd had a fight with Suzy the night

  before. I guess I'l have to try cycling in the cold again to find out.

  Good. But you're still looking for the cause, when it may be a cause. Another possible

  cause: Did you warm up first? Another possibility: You'll never know for sure.

  4. Write down a causal claim that you made recently and evaluate it. Have a classmate

  critique your evaluation.

  5. Make up three causal claims and trade with a classmate to analyze.

  6. Judge: I find that Nancy sustained serious injuries in this accident. There is suff
icient

  evidence that the defendant ran a red light and broadsided her car, causing the injuries.

  But I hold that Nancy was partly responsible for the severity of her injuries in that she

  was not wearing a seat belt. Therefore, Nancy shall collect only 50% of the costs

  associated with this accident.

  Explain the judge's decision in terms of normal conditions and foreseeable consequences.

  7. Mickey has taken his four-wheel-drive Jeep out into the desert to explore on this hot

  sunny Sunday. But his two cousins want to see him dead. Bertha has put poison in

  Mickey's five-gallon canteen. Richard, not knowing of Bertha's plans, has put a very

  small hole in the canteen.

  320 CHAPTER 15 Cause and Effect

  Mickey's car breaks down. He's getting hot and thirsty. His cellular phone doesn't

  work because he forgot to recharge it. He goes to get some water and finds the canteen

  empty. .. .

  Overcome by guilt later in the year, both Bertha and Richard confess. Who should

  be blamed for causing Mickey's death?

  D. Cause and Effect in Populations

  When we say "Smoking causes lung cancer," what do we mean? If you smoke a

  cigarette, you'll get cancer? If you smoke a lot of cigarettes this week, you'll get

  cancer? If you smoke 20 cigarettes a day for 40 years, you'll get cancer?

  It can't be any of these, since we know smokers who did all that yet didn't get

  lung cancer. And the cause always has to follow the effect. So what do we mean?

  Cause in populations is usually explained as meaning that given the cause,

  there's a higher probability that the effect will follow than if there were not the

  cause. In this example, people who smoke have a much higher probability of

  getting lung cancer than non-smokers.

  That's how it's explained. But really we are talking about cause and effect just

  as we did before. Smoking lots of cigarettes over a long period of time will cause

  (inevitably) lung cancer. The problem is that we can't state, we have no idea how to

  state, nor is it likely that we'll ever be able to state the normal conditions for smoking

  to cause cancer. Among other factors, there's diet, where one lives, exposure to

  pollution and other carcinogens, and one's genetic inheritance. But if we knew

  exactly, we'd say: "Under the conditions , smoking (number

  of) cigarettes every day for years will result in lung cancer."

  Since we can't specify the normal conditions, the best we can do is point

  to the evidence that convinces us that smoking is a cause of lung cancer and get an

  argument with a statistical conclusion: "People who continue to smoke two packs

  of cigarettes per day for ten years are % more likely (with a margin of error of

  %) to get lung cancer."

  What kind of evidence do we use?

  1. Controlled experiment: cause-to-effect

  This is our best evidence. We choose 10,000 people at random and ask 5,000 of

  them never to smoke and 5,000 of them to smoke 25 cigarettes every day. We have

  two samples, one composed of those who are administered the cause, and one of

  those who are not, the latter called the control group. We come back 20 years later

  to check how many in each group got lung cancer. If a lot more of the smokers got

  lung cancer, and the groups were representative of the population as a whole, and we

  can see no other common thread amongst those who got lung cancer, we'd be

  SECTION D Cause and Effect in Populations 321

  justified in saying that smoking causes lung cancer. The point of using a control

  group is to show that, at least statistically, the cause makes a difference.

  But we don't do such an experiment. It would be unethical. It's not acceptable

  to do an experiment on humans that has a (major) potential for doing them harm.

  So we use some animals sufficiently like humans that we feel are

  "expendable," perhaps rats. We fit them with little masks and have them breathe the

  equivalent of 25 cigarettes per day for a few years. Then if lots of them get lung

  cancer, while the ones who don't smoke are still frisky, we can conclude with

  reasonable certainty that smoking causes cancer in laboratory rats.

  So? We then argue that since rats are sufficiently similar to humans in their

  biological processes, we can extrapolate to say that smoking can cause cancer in

  humans. We argue by analogy.

  2. Uncontrolled experiment: cause-to-effect

  Here we take two randomly chosen, representative samples of the general population

  for which we have factored out other possible causes of lung cancer, such as working

  in coal mines. One of the groups is composed of people who say they never smoke.

  One group, comparable to the control group for controlled experiments, is composed

  of people who say they smoke. We follow the groups and 15-20 years later check

  whether those who smoked got lung cancer more often. Since we think we've

  accounted for other common threads, smoking is the remaining common thread that

  may account for why the second group got cancer more often.

  This is a cause-to-effect experiment, since we start with the suspected cause

  and see if the effect follows. But it is uncontrolled: Some people may stop smoking,

  some may begin, people may have quite variable diets—there may be a lot we'll

  have to factor out in trying to assess whether it's smoking that causes the extra cases

  of lung cancer.

  3. Uncontrolled experiment: effect-to-cause

  Here we look at as many people as possible who have lung cancer to see if there is

  some common thread that occurs in (almost all) their lives. We factor out those who

  worked in coal mines, those who lived in high pollution areas, those who drank a lot,

  . . . . If it turns out that a much higher proportion of the remaining people smoked

  than in the general population, we have good evidence that smoking was the cause.

  This is uncontrolled because how they got to the effect was unplanned, not

  within our control. And it is an effect-to-cause experiment because we start with

  the effect in the population and try to account for how it got there.

  How do we "factor out" other possible causes? How do we determine whether

  the sample of people we are looking at is large enough to draw conclusions about the

  general population? How do we determine if the sample is representative? How do

  we decide how many more cases of the effect—lung cancer—have to occur before it

  322 CHAPTER 15 Cause and Effect

  can be attributed to some cause rather than just to chance? These are the problems

  that arise whenever we generalize (Chapter 14), and only a course on statistics will

  make these issues clearer.

  Until you do take such a course and have access to actual write-ups of the

  experiments—not just the newspaper or magazine accounts—you'll have to rely on

  "the experts." If the experiment was done by a reputable group, without bias, and

  what we read passes the obvious tests for a strong generalization, a good analogy,

  and a good causal argument, then we can assume that the researchers know statistics

  well enough to conduct proper experiments—at least until some other reputable

  group challenges their results.

  Example 14 Reginald smoked two packs of cigarettes each day for thirty years.

  Reginald n
ow has lung cancer. Reginald's smoking caused his lung cancer.

  Analysis Is it possible for Reginald to have smoked two packs of cigarettes each

  day for thirty years and not get lung cancer? We can't state the normal conditions.

  So we invoke the statistical relation between smoking and lung cancer to say it is

  unlikely for the cause to be true and effect false.

  Does the cause make a difference? Could Reginald have gotten lung cancer

  even if he had not smoked? Suppose we know that Reginald wasn't a coal miner,

  didn't work in a textile factory, and didn't live in a city with a very polluted

  atmosphere—all conditions that are associated with a higher probability of getting

  lung cancer. Then it is possible for Reginald to have gotten lung cancer anyway,

  since some people who have no other risks do get lung cancer. But it is very

  unlikely, since very few of those people do.

  We have no reason to believe that there is a common cause. It may be that

  people with a certain biological make-up feel compelled to smoke, and that that

  biological make-up also contributes to their getting lung cancer independently of

  their smoking. But we have no evidence of such a biological factor.

  So assuming a few normal conditions, "Reginald's smoking caused his lung

  cancer" is as plausible as the strength of the statistical link between smoking and

  lung cancer, and the strength of the link between not smoking and not getting lung

  cancer. We must be careful, though, that we do not attribute the cause of the lung

  cancer to smoking just because we haven't thought of any other cause, especially if

  the statistical links aren't very strong.

  Example 15 Zoe: I can't understand Melinda. She's pregnant and she's drinking.

  Dick: That's all baloney. I asked my mom, and she said she drank

  when she was pregnant with me. And I turned out fine.

  Zoe: But think how much better you would have been if she hadn't.

  Analysis Zoe doesn't say but alludes to the cause-in-population claim that drinking

  during pregnancy causes birth defects or poor development of the child. That has

  been demonstrated: Many cause-in-population studies have been done that show

  EXERCISES for Section D 323

  there is a higher incidence of birth defects and developmental problems in children

  born to mothers who drink than to mothers who do not drink, and those defects and

 

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