Generalizing empirical findings to new environments, settings, or populations
is essential in most scientific explorations. This article treats a particular
problem of generalizability, called "transportability", defined as a license to
transfer information learned in experimental studies to a different population,
on which only observational studies can be conducted. Given a set of
assumptions concerning commonalities and differences between the two
populations, Pearl and Bareinboim (2011) derived sufficient conditions that
permit such transfer to take place. This article summarizes their findings and
supplements them with an effective procedure for deciding when and how
transportability is feasible. It establishes a necessary and sufficient
condition for deciding when causal effects in the target population are
estimable from both the statistical information available and the causal
information transferred from the experiments. The article further provides a
complete algorithm for computing the transport formula, that is, a way of
combining observational and experimental information to synthesize bias-free
estimate of the desired causal relation. Finally, the article examines the
differences between transportability and other variants of generalizability