11 October 2010 , 5.30 pm
Organised by Centre for Multilevel Modelling (CMM)Speaker: Professor Donald Rubin, Harvard University
Room SM1, Department of Mathematics, University Walk 5.30 – 6.30pm
For obtaining causal inferences that are objective, and therefore have the best chance of revealing scientific truths, carefully designed and executed randomized experiments are generally considered to be the gold standard. Observational studies, in contrast, are generally fraught with problems that compromise any claim for objectivity of the resulting causal inferences. The thesis here is that observational studies have to be carefully designed to approximate randomized experiments, in particular, without examining any final outcome data. Often a candidate data set will have to be rejected as inadequate because of lack of data on key covariates, or because of lack of overlap in the distributions of key covariates between treatment and control groups, often revealed by careful propensity score analyses. Sometimes the template for the approximating randomized experiment will have to be altered, and the use of principal stratification can be helpful in doing this. These issues are discussed and illustrated using the framework of potential outcomes to define causal effects, which greatly clarifies critical issues.
Professor Rubin is John L. Loeb Professor of Statistics at Harvard University. He has made, and continues to make, many important contributions to statistical methodology and its wider application. These include the Rubin Causal Model, propensity scores, and principal stratification, for the analysis of experiments and observational studies. All of these approaches are widely used in quantitative social science, the biomedical sciences, and beyond, and continue to underpin cutting edge research in the field of mathematical statistics.
Contact info-cmm@bristol.ac.uk
