Seminar 11th October 2010 5:30 p.m. University of Bristol
For Objective Causal Inference, Design Trumps Analysis (Bristol)
Professor Donald Rubin
Harvard University
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- Petrina Butler
Professor Donald Rubin talking on Causal Inference
We are pleased to announce that Professor Donald Rubin is visiting the University of Bristol on Monday 11 October.
He will be giving two talks on the subject of causal inference.
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.
For Objective Causal Inference, Design Trumps Analysis
5.30 – 6.30pm, room SM1, Department of Mathematics, University Walk
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.
Earlier in the day
Direct and Indirect Effects: An Unhelpful Distinction?
12.30 – 1.30pm, The Boardroom, 2 Priory Road, University of Bristol
The terminology of direct and indirect causal effects is relatively common in causal conversation as well in some more formal language. In the context of real statistical problems, however, I do not think that the terminology is helpful for clear thinking, and rather leads to confused thinking. This presentation will present several real examples where this point arises, as well as one that illustrates even the great Sir Ronald Fisher was vulnerable to such confusion.
Please contact info-cmm@bristol.ac.uk with any queries.
The Avon Local Group of the Royal Statistical Society
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The Avon Local Group provides an opportunity for statisticians in Bath and Bristol to meet and discuss statistical methods and their applications in areas of local interest, including healthcare, education, and the environment.