498 research outputs found
External Validity: From Do-Calculus to Transportability Across Populations
The generalizability of empirical findings to new environments, settings or
populations, often called "external validity," is essential in most scientific
explorations. This paper treats a particular problem of generalizability,
called "transportability," defined as a license to transfer causal effects
learned in experimental studies to a new population, 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 reduce questions of transportability to symbolic derivations in the
do-calculus. This reduction yields graph-based procedures for deciding, prior
to observing any data, whether causal effects in the target population can be
inferred from experimental findings in the study population. When the answer is
affirmative, the procedures identify what experimental and observational
findings need be obtained from the two populations, and how they can be
combined to ensure bias-free transport.Comment: Published in at http://dx.doi.org/10.1214/14-STS486 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org). arXiv admin note: text overlap with
arXiv:1312.748
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