The Rubin Causal Model is based in the idea of potential outcomes and the assignment mechanism: every unit has different potential outcomes depending on their "assignment" to a condition. For instance, someone may have one income at age 40 if they attend a private college and a different income at age 40 if they attend a public college. To measure the causal effect of going to a public versus a private college, the investigator should look at the outcome for the same individual in both alternative futures. Since it is impossible to see both potential outcomes at once, one of the potential outcomes is always missing. A randomized experiment works by assigning people randomly to (in this case) public or private college; because the assignment was random, the groups are (on average) equivalent, and the difference in income at age 40 can be attributed to the college assignment since that was the only difference between the groups.
The assignment mechanism is the explanation for why some units received the treatment and others the control. In observational data, there is a non-random assignment mechanism: in the case of college attendance, people may choose to attend a private versus a public college based on their financial situation, parents' education, relative ranks of the schools they were admitted to, etc. If all of these factors can be balanced between the two groups of public and private college students, then the effect of the college attendance can be attributed to the college choice.
Many statistical methods have been developed for causal inference, such as propensity score matching and nearest-neighbor matching (which often uses the Mahalanobis metric, also called Mahalanobis matching). These methods attempt to correct for the assignment mechanism by finding control units similar to treatment units. In the example, matching finds graduates of a public college most similar to graduates of a private college, so that like is compared only with like.
Causal inference methods make few assumptions other than that one unit's outcomes are unaffected by another unit's treatment assignment, the stable unit treatment value assumption (SUTVA).
Read more about Rubin Causal Model: An Extended Example, Matching, Conclusion, Relations To Other Approaches, See Also
Famous quotes containing the words rubin, causal and/or model:
“Women find ways to give sense and meaning to daily life—ways to be useful in the community, to keep mind active and soul growing even while they change diapers and cook vegetables.”
—Lillian Breslow Rubin (20th century)
“There is the illusion of time, which is very deep; who has disposed of it? Mor come to the conviction that what seems the succession of thought is only the distribution of wholes into causal series.”
—Ralph Waldo Emerson (1803–1882)
“When you model yourself on people, you should try to resemble their good sides.”
—Molière [Jean Baptiste Poquelin] (1622–1673)