Causal Arguments MILL’S METHODS AND SCIENTIFIC REASONING

Causal Arguments Causal arguments have the general form: A is correlated with B. Therefore, A causes, or helps cause, B.

Causal Arguments Example: Low speed limits are correlated with fewer traffic accidents. Therefore, low speed limits cause people to get into fewer traffic accidents.

Causation vs. Correlation  When determining the cause of a phenomenon, you have to discriminate between genuine causal relationships and incidental associations  Strong correlation between two sets of phenomena not enough to confirm a causal relationship between them  “Correlation does not imply causation”

Causation vs. Correlation A B number of fire trucks number of fires jury selection successful verdicts lice infestation physical health plaques in brain tissue Alzheimer’s disease ice cream purchases number of drownings depression surgery complications

Distinguishing Causation from Correlation There are a few reasons why you might find a correlation between two sets of phenomena. Causal reasons: 1) The first set is the cause of the second. 2) The second set is the cause of the first. 3) They are both causally linked to a third set of phenomena.

Distinguishing Causation from Correlation - Example: Correlation between ice cream consumption and drownings - Average ice cream consumption and drownings both causally linked to increased temperatures - more likely to go swimming or eat ice cream on a hot day.

Distinguishing Causation from Correlation  Confounding factors – additional variables that are correlated with the two factors, and may confuse the causal relationship.  Example: placebo effect  Example: the causal connection between eating meat and cardiovascular health is confounded by differences in other behaviors and traits in a population (such as exercise habits)

Distinguishing Causation from Correlation Non-causal reasons: 4) It is a coincidence that the two events are correlated.  There is no causal relationship between the pair of events at all – the correlation is a result of chance.

Coincidences can mimic causal relationships. Example:  Evolutionary anthropologists: wearing red gives athletes an edge over those who wear blue  Based this claim on data gathered at the 2004 Athens Olympics.  When experiment repeated at the 2008 Olympic games, effect not replicated (in fact, athletes who wore blue performed better)

Causation vs. Correlation Elderly people who have pets have been found to be significantly healthier than elderly people without pets. What are the possible explanations for this correlation?

Several possibilities: - Having a pet causes better health among the elderly. - The causal relationship is in the other direction: being healthy allows people to be able to be pet owners - A third factor is associated with both having a pet and better health: e.g. it is expensive to keep a pet, and money can pay for better medical care, so they are both positively associated with wealth - The correlation is just a coincidence

Suppose university students who take critical thinking are found to have a higher graduation rate than university students who do not. What are the possible reasons for this correlation?

Possibilities: - Taking critical thinking courses causes students to be more successful and more likely to graduate. - Being a successful student on the path to graduation makes you more likely to sign up for critical thinking courses than other students. - There is a third factor causally related to both – students who value critical thinking are more likely to want to take courses about it, and also are more inclined to develop the skills needed to successfully graduate. - It is a coincidence that the two are correlated; further studies would not be able to replicate the effect.

Questionable Causes  Causal claims are important, but we must consider them critically and be critical of causal claims drawn from insufficient evidence. Ex: Scientific Jury Selection  Cases with scientific jury selection are more likely to have favorable verdicts and a high success rate.  Does jury selection itself cause or contribute to causing the high success rate?

Avoiding Questionable Causal Attributions How do you determine the causal relationship between a pair of phenomena? John Stuart Mill provides us with some historically important methods for making causal inferences.

Mill’s Methods of Causal Inference 1. Method of Agreement  Two or more cases all experience effect E. One factor, F, is the only factor shared by the group.  Therefore F causes or contributes to causing E. X, F, Z  E A, C, F  E F causes E. F, D, P  E

Mill’s Methods of Causal Inference 1. Method of Agreement  Two or more cases all experience effect E. One factor, F, is the only factor shared by the group.  Therefore F causes or contributes to causing E. Example:  Rose, Donna, and Martha all order different entrees at dinner, and all get the pie for dessert.  All three get food poisoning.  The pie was the only thing in common.  Therefore, the pie made them sick.

Mill’s Methods of Causal Inference 2. Method of Difference  One case has effect E and the other does not experience effect E. The only difference between the groups: factor F is present in the case with E, and F is absent in the case without E.  Therefore, F is the cause (or contributory cause) of E. X, Y, Z, F  EF causes E. X, Y, Z  no E

Mill’s Methods of Causal Inference 2. Method of Difference  One case has effect E and the other does not experience effect E.  The only difference between the groups: factor F is present in the case with E, and F is absent in the case without E.  Therefore, F is the cause (or contributory cause) of E.

Mill’s Methods of Causal Inference 2. Method of Difference Example: - Jack plants two fields of tomatoes. He fertilizes only one. - The field that received the fertilizer grows. The field which didn’t get the fertilizer doesn’t grow. - Therefore, fertilizer causes the tomatoes to grow.

Mill’s Methods of Causal Inference 3. Joint Method of Agreement and Difference  The only difference between a group of cases with E and a group of cases without E, is that factor F is present in all of the cases with E, and F is absent in all of the cases without E.  Therefore, F is the cause (or contributory cause) of E. A B C D  no E Q X Y Z F  EF is the cause of E. A B C D F  E Q X Y Z  no E

Mill’s Methods of Causal Inference 3. Joint Method of Agreement and Difference Example: You work with a group of lab monkeys, subjected to a variety of experiments. About half of them suddenly become lethargic and obese; the others remain lean and active. Testing the lethargic & obese monkeys reveals their only shared trait is being infected with Adv36. When matched with lean, active monkeys subjected to identical conditions, the only differences found between them are that the lethargic, obese monkeys are infected and the lean monkeys are not. You conclude Adv36 caused the sudden lethargy and obesity.

Mill’s Methods of Causal Inference 4. Concomitant variation (for contexts where the factor interest F is always present)  In groups that are similar in all other important ways, as F increases, E increases and as F decreases, E decreases.  Therefore, F is the cause (or contributory cause) of E.

Mill’s Methods of Causal Inference 4. Concomitant variation - visualized:

Mill’s Methods of Causal Inference Concomitant variation example: A cluster of counties in southwestern Virginia maintains a steady deer population. Studies show that as the population of deer in an area increases, the number of car crashes increases as well. Additional studies show that when the deer population decreases, the amount of car crashes decreases. Therefore, a high deer population causes higher numbers of car crashes.

Mill’s Methods of Causal Inference 5. Method of Residue  A series of factors are believed to cause a series of phenomena.  We are able to match all the factors but one to their respective phenomena, and we are able to match all the phenomena but one to a factor.  Therefore, the remaining phenomenon E can be attributed to the remaining factor F.

Mill’s Methods of Causal Inference Method of Residue – example The new action movie performed well in terms of national box office sales, was well-received overseas, and was loved by critics. It was released at the peak of summer blockbuster season, was set in various international locations, and starred an Oscar winner. The timing of the release and the international locations are known to cause a movie to do well at the national box office and overseas. However, they are not sufficient for making critics enjoy a movie. Therefore, the presence of an Oscar-winning actor was the cause of the improved critical reception.

Causal Reasoning and Inference Since Mill proposed his methods, better statistical methods have been developed. However, it’s still an important historical example that helps us think about the way we make inferences about causes.