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

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Lee, Sanghack; Honavar, Vasant

Issue Date
Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, pp.583-590
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. Copyright © 2013, Association for the Advancement of Artificial Intelligence ( All rights reserved.
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  • Graduate School of Data Science
Research Area Causal Decision Making, Causal Discovery, Causal Inference


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