M-transportability: Transportability of a causal effect from multiple environments

Sanghack Lee, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

We study m-transportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m ≥ 1 source environments to estimate a causal effect in a given target environment. We provide a novel characterization of mtransportability that directly exploits the completeness of docalculus to obtain the necessary and sufficient conditions for m-transportability. We provide an algorithm for deciding mtransportability that determines whether a causal relation is m-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the desired causal effect by combining experimental information from m source environments with observational information from the target environment.

Original languageEnglish (US)
Title of host publicationProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Pages583-590
Number of pages8
StatePublished - Dec 1 2013
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: Jul 14 2013Jul 18 2013

Publication series

NameProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013

Other

Other27th AAAI Conference on Artificial Intelligence, AAAI 2013
Country/TerritoryUnited States
CityBellevue, WA
Period7/14/137/18/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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