Causation and Explanation in Social Science - Oxford Handbooks
There must be a one to one relationship between cause and outcome. The fifth criterion, biological gradient, suggests that a causal association is increased if. Humans depend upon causation all the time to explain what has happened to them, Taken together the evidence for a relationship between smoking and lung .. it is easy to calculate that C and W occur 5 percent of the time and that G and F . of the situation in order to meet the requirement for a “lawlike” relationship. Cause and effect is a relationship between events or things, where one is the result of the other I have 5 cavities. Cause and Effect Examples in Sentences .
Before you can show that you have a causal relationship you have to show that you have some type of relationship.Lesson 5 - Relationships II
For instance, consider the syllogism: I don't know about you, but sometimes I find it's not easy to think about X's and Y's. Let's put this same syllogism in program evaluation terms: This provides evidence that the program and outcome are related. Notice, however, that this syllogism doesn't not provide evidence that the program caused the outcome -- perhaps there was some other factor present with the program that caused the outcome, rather than the program.
Causation in epidemiology: association and causation
The relationships described so far are rather simple binary relationships. Sometimes we want to know whether different amounts of the program lead to different amounts of the outcome -- a continuous relationship: It's possible that there is some other variable or factor that is causing the outcome.
This is sometimes referred to as the "third variable" or "missing variable" problem and it's at the heart of the issue of internal validity. What are some of the possible plausible alternative explanations? Just go look at the threats to internal validity see single group threatsmultiple group threats or social threats -- each one describes a type of alternative explanation.
In order for you to argue that you have demonstrated internal validity -- that you have shown there's a causal relationship -- you have to "rule out" the plausible alternative explanations.
How do you do that? One of the major ways is with your research design.
Social Research Methods - Knowledge Base - Establishing Cause & Effect
Let's consider a simple single group threat to internal validity, a history threat. Let's assume you measure your program group before they start the program to establish a baselineyou give them the program, and then you measure their performance afterwards in a posttest.
You see a marked improvement in their performance which you would like to infer is caused by your program. One of the plausible alternative explanations is that you have a history threat -- it's not your program that caused the gain but some other specific historical event. For instance, it's not your anti-smoking campaign that caused the reduction in smoking but rather the Surgeon General's latest report that happened to be issued between the time you gave your pretest and posttest.
How do you rule this out with your research design? The judgement as to whether an observed statistical association represents a cause-effect relationship between exposure and disease requires inferences far beyond the data from a single study and involves consideration of criteria that include the magnitude of the association, the consistency of findings from other studies and biologic credibility .
The Bradford-Hill criteria are widely used in epidemiology as providing a framework against which to assess whether an observed association is likely to be causal.
Strength of the association. According to Hill, the stronger the association between a risk factor and outcome, the more likely the relationship is to be causal.
Have the same findings must be observed among different populations, in different study designs and different times? Specificity of the association. There must be a one to one relationship between cause and outcome.
Temporal sequence of association. Exposure must precede outcome.
Change in disease rates should follow from corresponding changes in exposure dose-response. Presence of a potential biological mechanism. Does the removal of the exposure alter the frequency of the outcome? According to Rothman , while Hill did not propose these criteria as a checklist for evaluating whether a reported association might be interpreted as causal, they have been widely applied in this way. Rothman contends that the Bradford - Hill criteria fail to deliver on the hope of clearly distinguishing causal from non-causal relations.
For example, the first criterion 'strength of association' does not take into account that not every component cause will have a strong association with the disease that it produces and that strength of association depends on the prevalence of other factors. In terms of the third criterion, 'specificity', which suggests that a relationship is more likely to be causal if the exposure is related to a single outcome, Rothman argues that this criterion is misleading as a cause may have many effects, for example smoking.