[gov3009-l] Don Rubin on "Design Trumps Analysis"

Justin Grimmer jgrimmer at fas.harvard.edu
Mon Oct 20 15:06:55 EDT 2008

Dear Applied Statistics Workshop,

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

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

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!


Justin Grimmer
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