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November 2014 External Validity: From Do-Calculus to Transportability Across Populations
Judea Pearl, Elias Bareinboim
Statist. Sci. 29(4): 579-595 (November 2014). DOI: 10.1214/14-STS486


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.


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Judea Pearl. Elias Bareinboim. "External Validity: From Do-Calculus to Transportability Across Populations." Statist. Sci. 29 (4) 579 - 595, November 2014.


Published: November 2014
First available in Project Euclid: 15 January 2015

zbMATH: 1331.62326
MathSciNet: MR3300360
Digital Object Identifier: 10.1214/14-STS486

Keywords: causal effects , Experimental design , External validity , generalizability

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.29 • No. 4 • November 2014
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