In recent years, job-training programmes have become increasingly important in many developed countries with rising unemployment. It is widely accepted that the best way to evaluate such programmes is to conduct randomized experiments. With these, among a group of people who indicate that they want job-training, some are randomly assigned to be offered the training and the others are denied it, at least initially. According to a well-defined protocol, outcomes such as employment statuses or wages for those who are employed are then measured for those who were offered the training and compared to the same outcomes for those who were not.
Despite the high cost of these experiments, their results can be difficult to interpret because of inevitable complications when doing experiments with humans. Three in particular are that some people do not comply with their assigned treatment, others drop out of the experiment before outcomes can be measured, and others who stay in the experiment are not employed, and thus their wages are not cleanly defined.
Statistical analyses of such data can lead to important policy decisions, and yet the analyses typically deal with only one or two of these complications, which may obfuscate subtle effects. An analysis that simultaneously deals with all three complications generally provides more accurate conclusions.
About the speaker
Don Rubin is John Loeb Professor of Statistics at Harvard University, and is a leading authority on causal inference in experiments and observational studies. In the 1970s he developed an influential common framework for causal analysis which continues to underpin cutting-edge research in both mathematical statistics and quantitative social science. The lecture will draw on his research in this field with examples from both social science and medicine.