M-Transportability: Transportability of a Causal Effect from Multiple Environments
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 targetenvironment. We provide a novel characterization of m-transportability that directly exploits the completeness of do-calculus to obtain the necessary and sufficient conditions for m-transportability. We provide an algorithm for deciding m-transportability 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.
Cite
Text
Lee and Honavar. "M-Transportability: Transportability of a Causal Effect from Multiple Environments." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8596Markdown
[Lee and Honavar. "M-Transportability: Transportability of a Causal Effect from Multiple Environments." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/lee2013aaai-m/) doi:10.1609/AAAI.V27I1.8596BibTeX
@inproceedings{lee2013aaai-m,
title = {{M-Transportability: Transportability of a Causal Effect from Multiple Environments}},
author = {Lee, Sanghack and Honavar, Vasant G.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2013},
pages = {583-590},
doi = {10.1609/AAAI.V27I1.8596},
url = {https://mlanthology.org/aaai/2013/lee2013aaai-m/}
}