Causes and Effects Help (page 3)
Introduction to Causes and Effects
When two things are correlated, it's tempting to think that there is a cause-and- effect relationship involved. Examples abound; anyone who listens to the radio, reads newspapers, or watches television these days can't escape them. Sometimes the cause–effect relationships aren't directly stated, but only implied. ''Take this pill and you'll be happy all the time. Avoid these foods and you won't die of a heart attack.''
Some of the cause–effect blurbs we hear every day must sound ridiculous to people who have grown up in cultures radically different from ours. ''Drink this fizzy liquid and you'll never get dirt under your fingernails. Eat this creamy white stuff and the hair on your ears will go away.'' The implication is always the same. ''Do something that causes certain people to make a profit, and your life will improve.'' Sometimes there really is a cause– effect relationship. Sometimes there isn't. Often, we don't know.
Correlation and Causation
Let's boil down a correlation situation to generic terms. That way, we won't be biased (or deluded) into inferring causation. Suppose two phenomena, called X and Y, vary in intensity with time. Figure 7-4 shows a relative graph of the variations in both phenomena. The phenomena change in a manner that is positively correlated. When X increases, so does Y, in general. When Y decreases, so does X, in general.
Is causation involved in the situation shown by Fig. 7-4? Maybe! There are four possible ways that causation can exist. But perhaps there is no cause-and-effect relationship. Maybe Fig. 7-4 shows a coincidence. If there were 1000 points on each plot, there would be a better case for causation. As it is, there are only 12 points on each plot. It is possible these points represent a ''freak scenario.'' There is also a more sinister possibility: The 12 points in each plot of Fig. 7-4 might have been selected by someone with a vested interest in the outcome of the analysis.
When we assign real phenomena or observations to the variables in a graph such as Fig. 7-4, we can get ideas about causation. But these ideas are not necessarily always right. In fact, intense debate often takes place in scientific, political, and even religious circles concerning whether or not a correlation between two things is the result of cause-and-effect, and if so, how the cause-and-effect actually operates. And how do we know that the data itself is not biased?
In the examples that follow, we'll rule out the bias factor and assume that all data has been obtained with the intent of pursuing truth. There are myriad ways in which data can be warped and rigged to distort or cover up truth, but we'll let sociologists, psychologists, and criminologists worry about that.
X causes Y
Cause-and-effect relationships can be illustrated using arrows. Figure 7-5A shows the situation where changes in phenomenon X directly cause changes in phenomenon Y. You can doubtless think of some scenarios. Here's a good real-life example.
Fig. 7-5A. At A, X causes Y.
Suppose the independent variable, shown on the horizontal axis in Fig. 7-4, is the time of day between sunrise and sunset.
Plot X shows the relative intensity of sunshine during this time period; plot Y shows the relative temperature over that same period of time. We can argue that the brilliance of the sunshine causes the changes in temperature. There is some time lag in the temperature function; this is to be expected. The hottest part of the day is usually a little later than the time when the sunshine is most direct.
It's harder to believe that there's a cause-and-effect relationship in the other direction. It is silly to suggest that temperature changes cause differences in the brilliance of the sunlight reaching the earth's surface. Isn't it? Maybe, but maybe not. Suppose heating causes the clouds to clear, resulting in more sunlight reaching the surface (Y causes X). Maybe there's something to this sort of argument, but most meteorologists would say that the former relation better represents reality. Bright sunshine heats things up. That's obvious.
Y causes X
Imagine that the horizontal axis in Fig. 7-4 represents 12 different groups of people in a medical research survey.
Each hash mark on the horizontal axis represents one group. Plot X is a point-to-point graph of the relative number of fatal strokes in a given year for the people in each of the 12 groups; plot Y is a point-to-point graph of the relative average blood pressure levels of the people in the 12 groups during the same year. (These are hypothetical graphs, not based on any real historical experiments, but a real-life survey might come up with results something like this. Medical research has shown a correlation between blood pressure and the frequency of fatal strokes.)
Is there a cause-and-effect relationship between the value of X and the value of Y here? Most doctors would answer with a qualified yes: variations in Y cause the observed variations in X (Fig. 7-5B). Simply put: high blood pressure can cause fatal strokes, in the sense that, if all other factors are equal, a person with high blood pressure is more likely to have a fatal stroke than a person identical in every other respect, but with normal blood pressure.
Fig. 7-5B. At B, Y causes X.
What about the reverse argument? Can fatal strokes cause high blood pressure (X causes Y)? No. That's clearly absurd.
Experts in meteorology and medical science who read this may, at this point, be getting a little nervous. Aren't the above scenarios oversimplified? Yes, they are. The cause-and-effect relationships described aren't ''pure.'' In real life, ''pure'' cause–effect events, where there is one certain cause and one inevitable effect, rarely occur.
The brightness of the sunshine is not, all by itself, the only cause-and-effect factor in the temperature during the course of a day. A nearby lake or ocean, the wind direction and speed, and the passage of a weather front can all have an effect on the temperature at any given location. We've all seen the weather clear and brighten, along with an abrupt drop in temperature, when a strong front passes by. The sun comes out, and it gets cold. That defies the notion that bright sun causes things to heat up, even though the notion, in its ''pure'' form where all other factors are equal, is valid. The problem is that other factors are not always equal!
In regards to the blood-pressure-versus-stroke relationship, there are numerous other factors involved, and scientists aren't sure they know them all yet. New discoveries are constantly being made in this field. Examples of other factors that might play cause-and-effect roles in the occurrence of fatal strokes include nutrition, stress, cholesterol level, body fat index, presence or absence of diabetes, age, and heredity. A cause-and-effect relationship (Y causes X) exists, but it is not ''pure.''
D causes both X and Y
Now suppose that the horizontal axis in Fig. 7-4 represents 12 different groups of people in another medical research survey. Again, each hash mark on the horizontal axis represents one group. Plot X is a point-to-point graph of the relative number of heart attacks in a given year for the people in each of the 12 groups; plot Y is a point-to-point graph of the relative average blood cholesterol levels of the people in the 12 groups during the same year. As in the stroke scenario, these are hypothetical graphs. But they're plausible. Medicine has shown a correlation between blood cholesterol and the frequency of heart attacks.
Before I make enemies in the medical profession, let me say that the purpose of this discussion is not to resolve the cholesterol-versus-heart-disease issue, but to illustrate complex cause–effect relationships. It's easier to understand a discussion about real-life factors than to leave things entirely generic. I do not have the answer to the cholesterol-versus-heart-disease riddle. If I did, I'd be writing a different book.
When scientists first began to examine the hearts of people who died of heart attacks in the early and middle 1900s, they found ''lumps'' called plaques in the arteries. It was theorized that plaques caused the blood flow to slow down, contributing to clots that eventually cut off the blood to part of the heart, causing tissue death. The plaques were found to contain cholesterol. Evidently, cholesterol could accumulate inside the arteries. Consulting data showing a correlation between blood cholesterol levels and heart attacks, scientists got the idea that if the level of cholesterol in the blood could be reduced, the likelihood of a heart attack would also go down. The theory was that fewer or smaller plaques would form, reducing the chances of clot formation that could obstruct an artery. At least, such was the hope.
The obvious first point of attack was to tell heart patients to reduce the amount of cholesterol in their food, hoping that this change in eating behavior would cause blood cholesterol levels to go down. In many cases, a low-cholesterol diet did bring down blood cholesterol levels. (Later, special drugs were developed that had the same effect.) Studies continue along these lines. It is becoming apparent that reducing the amount of cholesterol in the diet, mainly by substituting fruits, vegetables, and whole grains for cholesterol-rich foods, can reduce the levels of cholesterol in the blood. This type of dietary improvement can apparently also reduce the likelihood that a person will have a heart attack in the next year, or two, or three. There's more than mere correlation going on here. There's causation, too. But how much causation is there? And in what directions does it operate?
Let's call the amount of dietary cholesterol ''factor D.'' According to current popular medical theory, there is a cause-and-effect relation between this factor and both X and Y. Some studies have indicated that, all other things being equal, people who eat lots of cholesterol-rich foods have more heart attacks than people whose diets are cholesterol-lean. The scenario is shown in Fig. 7-5C. There is a cause-and-effect relation between factor D (the amount of cholesterol in the diet) and factor X (the number of heart attacks); there is also a cause-and-effect relation between factor D and factor Y (the average blood cholesterol level). But most scientists would agree that it's an oversimplification to say this represents the whole picture. If you become a strict vegetarian and avoid cholesterol-containing foods altogether, there's no guarantee that you'll never have a heart attack. If you eat steak and eggs every day for breakfast, it doesn't mean that you are doomed to have a heart attack. The cause-and-effect relationship exists, but it's not ''pure,'' and it's not absolute.
Fig. 7-5C. At C, D causes both X and Y.
Multiple factors cause both X and Y
If you watch television shows where the advertising is aimed at middle-aged and older folks, you'll hear all about cholesterol and heart disease – probably a good deal more than you want to hear. High cholesterol, low cholesterol, HDL, LDL, big particles, small particles. You might start wondering whether you should go to a chemistry lab rather than a kitchen to prepare your food. The cause-and-effect relationship between cholesterol and heart disease is complicated. The more we learn, it appears, the less we know.
Let's introduce and identify three new friends: factors S, H, and E. Factor S is named ''Stress'' (in the sense of anxiety and frustration), factor H is named ''Heredity'' (in the sense of genetic background), and factor E is named ''Exercise'' (in the sense of physical activity). Over the past several decades, cause-and-effect relationships have been suggested between each of these factors and blood cholesterol, and between each of these factors and the frequency of heart attacks. Figure 7-6 illustrates this sort of ''cause-and-effect web.'' Proving the validity of each link – for example, whether or not stress, all by itself, can influence cholesterol in the blood – is a task for future researchers. But all of the links shown in the diagram have been suggested by somebody.
The existence of correlation between two phenomena doesn't necessarily imply any particular cause-and-effect scenario. Two phenomena can be correlated because of a sheer coincidence. This is most likely to happen when the amount of data obtained is not adequate. In the case of blood cholesterol versus the frequency of heart attacks, test populations have traditionally contained thousands of elements (people). The researchers are justified in their conclusions that such correlation is not the product of coincidence. It is reasonable to suppose that some cause-and-effect interaction exists. Researchers are still busy figuring out exactly how it all works, and if they ever get it completely unraveled, it's a good bet that an illustration of the ''cause-and-effect web'' will look a lot more complicated than Fig. 7-6.
Correlation indicates that things take place more or less in concert with one another. This allows us to predict certain future events with varying degrees of accuracy. But there is another form of order that can be found in nature. This form of order, defined by a new science called chaos theory, illustrates that some phenomena, totally unpredictable and which defy statistical analysis in the short term or on a small scale, can nevertheless be highly ordered and predictable on a large scale. In a moment, we'll look at this.
Causes and Effects Practice Problems
What are some reasonable cause-and-effect relationships that might exist in Fig. 7-6, other than those shown or those directly between X and Y? Use arrows to show cause and effect, and use the abbreviations shown.
Consider the following. Think about how you might conduct statistical experiments to check the validity of these notions, and to determine the extent of the correlation.
- H → S (Hypothesis: Some people are born more stress-prone than others.)
- H → D (Hypothesis: People of different genetic backgrounds have developed cultures where the diets are dramatically different.)
- E → S (Hypothesis: Exercise can relieve or reduce stress.)
- E → D (Hypothesis: Extreme physical activity makes people eat more food, particularly carbohydrates, because they need more.)
- D → S (Hypothesis: Bad nutritional habits can worsen stress. Consider a hard-working person who lives on coffee and potato chips, versus a hard-working person who eats mainly fish, fruits, vegetables, and whole grains.)
- D → E (Hypothesis: People with good nutritional habits get more exercise than people with bad nutritional habits.)
What are some cause-and-effect relationships in the diagram of Fig. 7-6 that are questionable or absurd?
Consider the following. Think about how you might conduct statistical experiments to find out whether or not the first three of these might be moved into the preceding category.
- H → E (Question: Do people of certain genetic backgrounds naturally get more physical exercise than people of other genetic backgrounds?)
- S → E (Question: Can stress motivate some types of people to exercise more, yet motivate others to exercise less?)
- S → D (Question: Do certain types of people eat more under stress, while others eat less?)
- S → H (Obviously absurd. Stress can't affect a person's heredity!)
- E → H (Obviously absurd. Exercise can't affect a person's heredity!)
- D → H (Obviously absurd. Dietary habits can't affect a person's heredity!)
Practice problems for these concepts can be found at:
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