chapter 7.2 Flashcards
millls method of concomitant variations
This method begins with the observation of correlation: that the values of two variables change in the same circumstances. mills noted that when one variable varies together with another, we may infer a causal connection of some kind between them, although we wont yet know just how they are causally related. More specifically we wont yet know whether the two variables are cause and effect, or share a common cause or are related in some other way.
E.g. we might see that people who play more video games than average are also more violent than average. But while these attributes may be causally related we cannot tell just from their concomitant variation whether propesity to violence causes an interest in video games whether people become more violent by virtue of the game exposure or whether there is some indirect relationship between them
Method of agreement
one begins with cases
that agree in effect, and then scrutinizes them to learn what possible cause they have in
common—some way in which they agree. If in all instances when an effect occurs there
is one prior event or condition common to all of those cases, then one may infer that the
event is the cause of the effect. To use this method, one might let the causal background
vary while keeping the suspected cause the same. If the suspected effect still occurs in
those different instances, this is evidence that the suspected cause is indeed responsible
for the effect. If the causal background is varied suffi ciently, this rules out a common
cause or other circuitous causal relationship.
When trying to understand the cause of a particular event or phenomenon, the method of agreement looks at different instances where that event occurs.
The key idea is to find what these instances have in common, especially in terms of conditions or factors.
By identifying the common factor present in all instances where the event occurs, it is hypothesized that this common factor is a potential cause or contributor to the event
method of difference
It begins with cases that differ in
effect, and then scrutinizes them to learn whether there’s some other respect in which
they differ. If in one case an effect is observed and in another case that effect is not
observed, and the only difference is the presence of a single event or condition in the fi rst
case that is absent in the second case, then one may infer that this event is the cause of
the effect. An instance in which the suspected effect occurs is compared to an instance in
which the suspected effect does not occur. If the suspected cause is the only factor present
in the former but not the latter, this suggests the suspected causal relationship obtains.
Joint method of agreement and difference
We can consider cases where
the suspected effect occurs and see what they have in common and consider also cases
where the suspected effect does not occur and see what those have in common. If the
suspected cause is the only difference between the two sets of cases, then this affi rms
a causal relationship between the suspected cause and the suspected effect. Imagine
interviewing people with a record of violence and people without such a record. If the
only distinguishing feature we fi nd is that those in the former group play a lot of video
games and those in the latter group do not, this result would indicate a causal connection between video games and violence. This joint method of agreement and difference
provides more evidence of the causal relationship than either the method of agreement
or the method of difference by itself.
Method of residues
is a way to apportion causal responsibility. With this
method, one traces all other effects to their causes and looks for the causal variable that
remains. If scientists have learned that some causal factors bring about certain effects, and
some of those causes present by themselves bring about some but not all of the effects,
then the missing cause(s) should be taken to be responsible for the absent effect(s).
This is a way of taking into account the causal background in order to focus on some
specifi c cause and determine the difference it makes. Imagine we’ve learned that obesity
and smoking cause diabetes, heart disease, and lung cancer. From our knowledge that
obesity causes diabetes and heart disease but not lung cancer, we can infer that smoking
causes lung cancer. A limitation of this form of causal reasoning is that it assumes causal
relationships are simpler than they often are. What if, for example, the combination of
obesity and smoking together causes lung cancer, but neither does by itself? The method
of residues can’t evaluate this possibility
