Here’s a clever idea —
Let’s say you want to predict who dies within the next year (I bet you are a lot of fun at parties). Moreover, you have a hypothesis to test that married people are less likely to die than single people. There are a number of factors that relate to both marriage and death. Married people see the doctor more often, they report less depression and are more likely to be employed. People who receive less health care, are unemployed and depressed are all more likely to die. Sucks to be you.
You could do an analysis where you control for doctor visits, depression and employment. OR, you could create a propensity score, using all three of those variables and predict how likely a person is to get married, also known as a propensity score. Then you could match people on propensity scores, which effectively controls for all of those other variables related to marriage. In very simple terms, this would be kind of like having equal numbers of unmarried, depressed, employed people and married, depressed, employed people all of whom haven’t seen a doctor in a decade.
Since you have controlled for these other related factors, you could then see if marriage is really related to lower mortality. I threatened my late husband that if he died and left me with all of these young children that I would spend all of our money on gigolos in the Bahamas.
I thought that perhaps threats like these would be related to fewer deaths among married people. In my case, it did not work, he died anyway and I did go to the Bahamas eventually, with my new husband who is a software engineer trained as a physicist. No gigolos were involved. I am not certain if that is a good thing or a bad thing.
If you are interested in learning about computing and using propensity scores with SAS, you can find more information here.