Background
Rubin, Donald Bruce was born on December 22, 1943 in Washington, District of Columbia, United States. Son of Allan A. and Harriet (Schainis) Rubin.
educator statistician research company executive
Rubin, Donald Bruce was born on December 22, 1943 in Washington, District of Columbia, United States. Son of Allan A. and Harriet (Schainis) Rubin.
As an undergraduate Rubin attended the accelerated Princeton University Doctor of Philosophy program where he was one of a cohort of 20 students mentored by the physicist John Wheeler (the intention of the program was to confirm degrees within 5 years of freshman matriculation). He switched to psychology and graduated in 1965.
He was hired by Harvard in 1984, and served as chair of the department from 1985-1994. He is most well known for the Rubin Causal Model, a set of methods designed for causal inference with observational data, and for his methods for dealing with missing data. He began graduate school in psychology at Harvard with a National Science Foundation fellowship, but because his statistics background was considered insufficient, he was asked to take introductory statistics courses.
Rubin felt insulted by this given his background in physics, so he decided to transfer to applied math, as he says in the introduction to Matched Sampling for Causal Effects.
He received his Master of Arts Rubin became a Doctor of Philosophy student again, this time in Statistics under William Cochran at the Harvard Statistics Department. After graduating from Harvard in 1970, he began working at the Educational Testing Service in 1971, and served as a visiting faculty member at Princeton"s new statistics department.
He published his major papers on the in 1974–1980, and a textbook on the subject with econometrician Guido Imbens to be published in May, 2015. Rubin later moved to the University of Wisconsin–Madison, the University of Chicago, and Harvard.
The is based on the idea of potential outcomes and the assignment mechanism: every unit has different potential outcomes depending on their "assignment" to a condition.
Foreign 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. lieutenant is obviously impossible to see both potential outcomes at once, and 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, et cetera
If all of these factors can be balanced between the two groups of public and private college students, then in Rubin"s model the effect of college attendance can be attributed to the college choice.
(Demonstrates how nonresponse in sample surveys and census...)
(Praise for the First Edition of Statistical Analysis with...)
Fellow American Association for the Advancement of Science (chairman statistics 1992), American Statistical Association (editor journal 1980-1982, director 1980-1982, statistician of year Boston chapter 1995, S.S. Wilks medal 1995), Institute Mathematics Statistics (county member 1990-1992). Member NAS (commission on national statistics 1989-1992, member panel on confidentiality data 1989-1992, panel on bilingual education 1990-1992, working group on statistical analysis of commission on basic research in behavioral and social science 1985-1986, panel statistical in 21st century 1995, other committees), American Association for the Advancement of Science, American Academy Arts and Science, Biometric Society, International Association Survey Statisticians, International Statistical Institute, Psychometric Society, Royal Statistical Society.
Married Kathryn M. Kazarow. Children: Scott Wilk, Paul Stuart.
