  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.
