Establishing Causation Chap 4.3 Establishing Causation

Causation One of the hardest question to answer though, is whether or not the explanatory variable really causes a change in the response variable. In this section we will look at how to try and determine if there really is Causation.

Commonly Asked Questions What ties between two variables(and other possible lurking variables) can explain an observation? What constitutes good evidence for causation? Look at and discuss examples of observed associations between x and y variables on page 306.

Consider the list of 16 factors, 8 of which show a strong correlation (positive or negative) with test scores. The other 8 don’t seem to matter. Which do ones do you think are which. The child has highly educated parents. The child’s family is intact. The child’s parents have high socioeconomic status. The child’s parents recently moved into a better neighborhood. The child’s mother was 30 or older at the time of the first child’s birth The child’s mother didn’t work between birth and kindergarten. The child had a low birth weight. The child attended Head Start. The child’s parents speak English in the home. The child’s parents regularly take them to museums. The child is adopted. The child is regularly spanked. The child’s parents are involved in the PTA. The child frequently watches TV. The child has many books in the home. The child’s parents read to him nearly every day. The odd numbered factors correlate with test scores, the even ones do not.

Explaining Association: Causation: Cause and Effect link between the two variables. Could possibly include lurking variable as well. Common Response: The observed association between x and y is explained by a lurking variable z. Both x and y change in response to changes in z. This common response creates an association even though there may be no direct causal link between x and y. Confounded: Two variable (whether explanatory variables or lurking variables) are confounded when their effects on a response variable are mixed together. When many variables interact with each other, confounding of several variables can prevent us from drawing conclusions about causation.

Examples of Associations Causation: Amount of calories you consume affects the amount of weight. There is causation there, but it is not total. Other variables such as amount of exercise, lifestyle, or genetics play a part as well. Common Response: A student’s score on the SAT verbal and SAT math are correlated. Generally good students score better on both, than poor students, so the quality of the student would be a common response variable in this situation more than the score of one or the other. Confounding: People who are active in their religion generally live longer than non-religious people. The confounding variables in this situation are life-style choices. People active in religion tend to smoke and drink alcohol less. They also tend to take better care of their bodies due to religious beliefs.

Confounding It is likely that more education is a cause of higher income. However, confounding is present. People who have high ability and come from prosperous homes are more likely to get many years of education than people who are less able or poorer. People who start out rich are more likely to have high earnings even without much education. We can’t really say how much of the higher income of well educated people is actually caused by their education.

Establishing Causation Most important lesson of these examples: Even a very strong association between two variables is not by itself good evidence that there is a cause-and-effect link between the variables. The best way to establish a direct causal link between x and y is to conduct a carefully designed experiment in which the effects of possible lurking variables are controlled. (Not always possible…think about smoking causing lung cancer)

Continued If an experiment is not possible, the following is the criteria that should be used to determine a direct casual link between x and y. The association is strong. The association is consistent. Higher doses are associated with stronger response. The alleged cause precedes the effect in time. The alleged cause is plausible.