Don Rubin on "Design Trumps Analysis" 

 Please note, there has been a scheduling change.  Kosuke Imai, Department of Politics, Princeton University, will be presenting on November 12th.  

 In Kosuke's place, this wednesday, October 22nd,  Don Rubin, Professor of Statistics, Harvard University, will present his paper, "For Objective Causal Inference, Design Trumps Analysis".  Don provided the following abstract: 

 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. 

 Don has provided the full paper available  here  . 
  
The applied statistics workshop meets at 12 noon in Room K-354, CGIS Knafel (1737 Cambridge St), with a light lunch. Our presentations begin at 1215 and usually conclude around 130 pm. As always, everyone is welcome!