For objective causal inference, design trumps analysis.

*(English)*Zbl 1149.62089Summary: 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.

##### MSC:

62P10 | Applications of statistics to biology and medical sciences; meta analysis |

62P99 | Applications of statistics |

##### Keywords:

average causal effect; causal effects; complier average causal effect; instrumental variables; noncompliance; observational studies; propensity scores; randomized experiments; Rubin causal model; breast cancer; marketing; cardiac cancer
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\textit{D. B. Rubin}, Ann. Appl. Stat. 2, No. 3, 808--840 (2008; Zbl 1149.62089)

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