Causation. Producing vs. Predicting

Transcription

1 Causation Producing vs. Predicting 1

2 What is Causation? Cause and effect is a concept that scientists use a lot, but it is also a common sense notion. Many ordinary verbs express causal relations, e.g. he threw the ball to me, She knocked over the vase and broke it, I painted that picture, etc. 2

3 The basic idea of cause and effect is that some objects and events come from, or owe their existence to other objects and events. In short, causation is production. 3

4 Causes vs. Indicators/Predictors Many yscientific c studies try tytoto find out what makes aes children do well in school. Some such studies found that children who grow up in homes with a lot of books in them do better in school. Interestingly, the child s academic performance is more closely linked to (better predicted by) the number of books in the house than to the amount of time the child spends either reading the books or being read to by the parent. 4

5 Magical osmosis? Do we conclude that, by some hidden mechanism, the child s mere proximity it to books causes them to become smarter? Some politicians haveadvocatedadvocated delivering truck loads of books to the houses of under achievers, to boost their academic performance! 5

6 No. It s more likely that the number of books in the child s house is a mere indicator, or predictor, of success rather than a cause. Parents who are themselves intelligent or well educated tend to have more books. And their intelligence gets transmitted to the kids (by genetic inheritance, parenting, or both). This leads to a positive ii correlation between books and academic success. 6

7 Correlation due to common cause 7

8 Positive Correlation Note that if one factor A is a predictor (i.e. indicator) for B, then B is also a predictor for A. It goes both ways, or neither. Each is factor is positively relevant to the other. We often say that the factors are positively correlated. Causation, by contrast, is a one way relation. If A caused B, then B did not cause A. 8

9 Smoking and cancer Studies show that smokers have a higher rate of lung cancer than non smokers do. Smoking is therefore an indicator or predictor for lung cancer. And, therefore, lung cancer is an indicator for smoking. But what s the causal connection here? 9

10 The Typical Causal Connections 10

11 It often takes a lot of work to figure out which of these is actually the case. In particular, tobacco companies argued for many years that smokers are just different, ie i.e. they have some X factor that causes them to smoke, and which (independently) also causes lung cancer. 11

12 What do graphs like this show? 12

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14 Can you see one thing causing another? Obviously we can t directly see smoke in the lungs causing cancer. The events are microscopic, and hidden inside thebody. But can we even see causation on the macroscopic level? E.g. A moving billiard ball strikes a stationary one, causing it to move. Can we see the causation here? 14

15 According to Hume, we cannot see the causation itself. We just see the motions of the balls. We see the first ball moving, then see it come into contact with the second ball, and then we see the second ball begin to move. Is Hume right about this? One can create the illusion of cause and effect by (e.g.) swinging one s fist very close to someone else s jaw, making a loud d crack sound, and then having them yanked backward by a hidden cord. It looks as if they were knocked backward by the fist. (How do we know that something similar isn t happening with the billiard balls?) 15

16 It seems that Hume is right. But Hume went further with this idea, and claimed that our concept of cause and effect is just the concept of one thing happening and then another, regularly, so that fromseeing the first we infer (predict, or expect) the second. Is this correct? 16

17 Hume s view here seems to ignore the difference (discussed above) between causes and indicators. Non empiricists (i.e. rationalists) draw the opposite conclusion fromthis fact that we cannot see causation. They reason that the concept of cause and effect must be innate, which means that it is hard wired into the human brain, and not derived fromsense experience. Immanuel Kant, for example, took this view. 17

18 Causation and Inference We have seen that causation is quite different from inference (= prediction, indication, etc.) After all, if smoking is the main cause of lung cancer, and we see a lung cancer patient, then we can infer that they (probably) smoked. We say, He s probably a smoker, because he has lung cancer. But that doesn t mean that the lung cancer caused the smoking! 18

19 Causation and Inference But despite this general separation between causation and inference, some philosophers believe that (in principle at least) one can always infer an effect from a complete description of its causes. In other words, the effect of a given (complete) cause is always necessary. It had to occur, once the causes came to exist. This view is called causal determinism. i 19

20 Causal Determinism According to causal determinism, if you do the exact same experiment twice, precisely replicating all theconditions (causes), then you will get the same outcome. According to indeterminists, on the other hand, exactly similar experiments can yield different outcomes. (But the chance of an outcome must be the same in both experiments.) 20

21 Are indeterminists crazy? 21

22 Stochastic Causation Event A stochastically causes B if and only if A causes B, but the occurrence of B cannot be inferred with certainty from any description of A. An event with a stochastic cause is said to be random. Note that, when some philosophers talk of random events, they mean events that are uncaused. In other words, the event is not produced by anything at all; it simply appears, spontaneously, from nowhere. The existence of such events is extremely dubious, however. I know of no evidence that such events exist. 22

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24 Stochastic Causation Quantum mechanics cs (presently the ebest theory yof small things like atoms) presents the world as being stochastic. Is quantum mechanics therefore incomplete? Is it failing to describe some things going on in the world? (This was Einstein s i view.) Perhaps. But interestingly, this situation seems to be permanent. There s good evidence that no other theory will ever do better. QM seems to be a maximal theory. 24

25 Physical Chance Assuming that there is stochastic causation, i.e. there are causes which do not determine their effects, we can define a kind of probability called physical chance. The physical chance of an event is the degree to which it is determined by its (possible) causes. E.g. if the event is determined, then its chance is one. But if it is only almost determined, then its chance is lower, perhaps Something that is determined not to occur has a chance of zero. 25

26 Positive Causal Factors If physical ysca chances cesexist st(etssuppose (let s suppose they eydo) then we can consider how chances change when factors are added or subtracted. For example, suppose that smoking causes lung cancer stochastically. Whether or not a given person gets cancer is then a matter of chance. Yet, we can say that smoking cigarettes increases the chance that a given person will get lung cancer. Smoking is a positive causal factor for lung cancer. 26

27 Negative Causal Factors A positive causal factor is a chance raiser. A negative causal factor for cancer, by contrast, reduces the chance of getting cancer. E.g. eating certain fresh vegetables, rich in antioxidants, id is apparently a negative causal factor for cancer. Regular exercise is a negative causal factor for strokes and heart disease. 27

28 Causal Factors and Indicators 28

29 Strong vs. Weak Factors If the increase in chance is large, g, say from 0.1 to 0.7 then we have a strong (positive) causal factor. A small increase, say from 0.3 to 0.31, constitutesa weak causal factor. Note that the strength depends on the difference betweenthe the chances,notnot theirratio. ratio. For example, an increase in one s chance (of dying in a house fire, say) from to is tiny, even though it is an increase of 300%. On the other hand, an increase from 0.5 to 0.6 is huge, even though it is only 20% more. 29

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31 For only $100, you can have a quick, painless test for gum cancer. And early detection of cancer is crucial to successful treatment. Studies show that having the test actually reduces your chance of dying of gum cancer by over 60%. Is getting the test worth $100? 31

32 Causes and Positive Causal Factors For the most part, we can pretend that causes and positive causal factors are the same thing. But consider this case. Jim has a very severe bacterialinfection infection, so that he s in grave danger. Untreated, the infection has a 60% chance of killing him. Jim s desperate doctors decide to give him a massive dose of a powerful intravenous antibiotic. This antibiotic is for extreme circumstances only, as there s a 10% chance that the antibiotic itself will kill the patient. That s what happens in Jim s case. The infection is dealt with, but as an unfortunate side effect Jim dies. 32

33 Did the antibiotic treatment kill Jim? (Did it cause his death?) Did theantibiotic increase thechance that Jim would die? 33

34 Causes and Positive Causal Factors 4. When one crosses an ocean by ship, there are usually lifeboats on board, to be used in case of emergency. On one ocean voyage you took, such lifeboats were present, but were not used as the ship didn t sink, catch fire, or any such thing. (i) Were the lifeboats a partial cause of your safe return to dry land in this case? (ii) Was the presence of the lifeboats a positive ii causal factor for your return to dry land? 34

35 People on anti depressants are actually more likely to commit suicide than those who aren t t. My advice is to stop taking those things right away. 35

36 Being a soldier at war is actually much safer than people think. The death rate for US soldiers during thespanish American war, for example, was lower than the death rate among residents of New York City, during the same period. 36

37 Causal paradox? A certain drug both causes blood clots and is a negative causal factor for blood clots. How is this possible? 37

38 1. You know the joke about the bad surgeon: Surgeon: I saved your life today. Patient:?? But you cancelled my operation today. Surgeon: Exactly. (i) Did this surgeon s (in)action cause the patient to continue living? i (ii) Did the surgeon s s (in)action increase the chance of the patient living? 38

39 Explanation There s a strong link between A causing B and A explaining B. Eg E.g. Newton explained the tides by showing that they were caused by the moon s gravitational pull. In general, we explain something by saying what caused it. 39

40 Explanans and Explanandum Newtonsaid that themoon s gravity explains the tides. In this case the (observed) tides are called theexplanandum explanandum (thething thing explained) and the moon s gravity is the explanans (the thing that does the explaining). In general, the explanans is a cause of the explanandum. 40

41 Explanation and Inference But there s a bit more to explanation than just pointing to a cause. When Newton explained the tides, for example, he did more than just say h the moon is doing it What more did he have to do? 41

42 He had to show that, assuming his theory of gravity, and certain features (e.g. mass) of the moon, one would expect 2 tides per 24 hour period. And one would expect the oceans to rise and fall so many feet. In short, he had to infer or predict the tides fromhis (supposed) cause, i.e. themoon s gravity. 42

43 Why are there two high tides per 24 hours, not just one? 43

44 Soan explanation includes an inference, or argument, of the following form: (Alleged) Cause (explanans) (Observed) Effect (explanandum) 44

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46 An event with a stochastic cause is therefore impossible to explain, or at least to fully explain. Stochastic events cannot be (fully) predicted fromtheir causes. 46

47 Is Chance itself a cause? Can you explain an event as being the result of chance? You can at least say that the event is the result of a stochastic cause. Note however that such a proposed cause only predicts the effect to a low degree. 47