Causal Transportability with Limited Experiments

author: Elias Bareinboim, Computer Science Department, University of California, Los Angeles, UCLA
published: Oct. 6, 2014,   recorded: December 2013,   views: 26
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Description

Most scientific explorations are concerned with generalizing empirical findings to new environments, settings, or populations, a problem that the machine learning literature labeled "transfer learning." Our talk focuses on a particular type 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”.

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