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

Sanghack Lee, Vasant Honavar

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

10 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
CountryUnited States
CityBellevue, WA
Period7/14/137/18/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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    Lee, S., & Honavar, V. (2013). M-transportability: Transportability of a causal effect from multiple environments. In Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013 (pp. 583-590). (Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013).