Department of Statistics, UCLA

We provide a formal definition of the notion of “transportability,” or “external validity,” which we view as a license to transfer causal information learned in experimental studies to a different environment, in which only observational studies can be conducted. We introduce a formal representation called “selection diagrams” for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we derive procedures for deciding whether causal effects in the target environment can be inferred from experimental findings in a different environment. When the answer is affirmative, the procedures identify the set of experimental and observational studies that need be conducted to license the transport. We further demonstrate how transportability analysis can guide the transfer of knowledge among non-experimental studies to minimize re-measurement cost and improve prediction power. We further provide a causally principled definition of “surrogate endpoint” and show that the theory of transportability can assist the identification of valid surrogates in a complex network of cause-effect relationships.