Events
Economics/Stats Seminar
Location: S2.79
Donald Rubin (Harvard)
For Objective
Causal Inference, Design Trumps Analysis
For obtaining causal inference 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.