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For obtaining causal inferences that are objective, and therefore have thebest chance of revealing scientific truths, carefully designed and executedrandomized experiments are generally considered to be the gold standard.Observational studies, in contrast, are generally fraught with problems thatcompromise any claim for objectivity of the resulting causal inferences. Thethesis here is that observational studies have to be carefully designed toapproximate randomized experiments, in particular, without examining any finaloutcome data. Often a candidate data set will have to be rejected as inadequatebecause of lack of data on key covariates, or because of lack of overlap in thedistributions of key covariates between treatment and control groups, oftenrevealed by careful propensity score analyses. Sometimes the template for theapproximating randomized experiment will have to be altered, and the use ofprincipal stratification can be helpful in doing this. These issues arediscussed and illustrated using the framework of potential outcomes to definecausal effects, which greatly clarifies critical issues.

For objective causal inference, design trumps analysis